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INTEGRATING IMAGE SEGMENTATION AND CLASSIFICATION USING TEXTURE PRIMITIVES FOR NATURAL AND AERIAL IMAGES

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INTEGRATING IMAGE SEGMENTATION AND CLASSIFICATION USING TEXTURE PRIMITIVES FOR NATURAL AND AERIAL IMAGES Powered By Docstoc
					    International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
       Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856




         INTEGRATING IMAGE SEGMENTATION
         AND CLASSIFICATION USING TEXTURE
            PRIMITIVES FOR NATURAL AND
                  AERIAL IMAGES
                                       S. RIZWANA1, Dr. S. PANNIRSELVAM2
                                                     1
                                                      Research Scholar,
                                         Manonmaniam Sundaranar University, Tirunelveli.
                                                   2
                                                    Associate Professor & HOD,
                                               Erode Arts and Science College, Erode

                                                                  real-world objects. Region-based image segmentation is
Abstrac : Image segmentation and classification is a              an approach to image segmentation, in which an image is
preliminary and critical step in image processing analysis. Its   divided into associated regions by aligning adjoining
appropriate valuation guarantees that the finest segmentation     pixels of analogous features, and closest regions are then
outcome is employed in image classification. A key feature of     combined beneath some standard for instance the
semantic image segmentation is to incorporate restricted and      homogeneity of features in adjoining regions. Interesting
inclusive features for the calculation of local segment labels.   features comprise texture, color, shape, etc. To attain
An approach is presented here to multi-part segmentation
                                                                  fine-grain segmentation at the pixel point, we ought to be
which integrates two methods: semantic based image
segmentation for texture extraction with Regional and
                                                                  capable to describe features for instance color and texture
Multitude techniques involved in it and an image                  on a per-pixel basis. Selling with information removed
classification method which regards as inclusive features. In     from a natural image, satellite data, a medical scan or a
this paper we present a technique for incorporating a set of      frame in a video series is the principle of image
images, following the methods of image segmentation and           examination. In the genuine world, the incentive that is
classification. The proposed multi-part image segmentation        established by the image object is alleged as complete and
analysis architecture combines algorithms for three levels:       absolute information.
i) a multitude provincial texture feature drawing out             The purpose of segmentation methods is to establish a
technique based on the segmentation of natural and aerial         separation of an image into a restricted number of
images. Image granularity textures are used for                   semantically significant regions for instance anatomical
homogeneous regions. Then a confined threshold texture
                                                                  or practical structures in medical images or objects in
segmentation for images is applied. Here extraction is
                                                                  natural images. The segmentation job has been
proceeded with proportions analogous to the tarnish size are
to be extracted. ii) mid-level phase, segmented images are        considered for numerous decades; though it is still a
classified using texture primitives and, iii) rich level, i.e.    demanding job. This task is necessary in several
extraction of requisite information from the classified image.    applications counting face recognition in video sequences,
By the way, the user can extract description of rare              changes recognition in satellite images, anatomical or
multimedia process and utilize it, backed up by a knowledge       functional object taking out in medical images or object
repository. An experimental evaluation is conducted with          extraction in natural images. The assortment of processes
training samples of images to demonstrate the performance         concerned in the visual observation is frequently
of the proposed IISCTP scheme for natural and aerial images       classified as either low level vision or high level vision.
and compare the results with the existing classification of       High level vision comprises of the elucidation of the
segmented images for analysis using hybrid methodology and        image subsequent to some earlier knowledge. In low level
Multitude Regional Texture Extraction for Image
                                                                  vision, image processing is achieved to dig out some
Segmentation.
                                                                  observable physical properties in the image such as shape
Keywords:      texture     feature     extraction,      image
                                                                  and margins or to progress the feature of the image.
segmentation, classification, texture primitives
                                                                  A general segmentation technique, which executes fine in
                                                                  several contexts, does not subsist. The techniques usually
                                                                  reliant on:
1. INTRODUCTION                                                         The processing of the image: richness in textures
Image segmentation is a primary course in several                         with diverse orientations and/or scales, unclear
computer vision applications. It divides an image into                    conversions among regions, occulted contours, etc.,
numerous components, which preferably correspond to

Volume 1, Issue 4 November - December 2012                                                                         Page 12
   International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
       Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856


     Extraction of features: edges (steps, lines,           recognition and early discovery of diseases. We have
      junctions), consistent regions in the logic of         utilized a diverse method to categorize the medical image
      textures, grey levels, forms (curves, etc.), etc.,     so as to assist the former recognition of mass. The
     Issues in segmentation: 3D renovation, pattern         approach in [6] mines the mixture feature set from the
      detection, image sympathetic, computerized object      preprocessed and segmented image. For feature and
      tracking, etc.,                                        categorization of image, the author in [7] presented new
     The      utilization    constraints:   computational   particle swarm optimization technique. Automated seeded
      difficulty, real time operation, materials             lesion segmentation [8] is also being used with the
      restrictions associated to the attainment systems,     classification of image under different aspects.
      memory capability, etc.                                Several techniques have been presented to perform the
                                                             image segmentation in a reliable way. The most
In this work, a semantic integrating of images is done for   widespread method utilized for medical image
image segmentation and classification. The proposed          segmentation is holding up vector mechanism [9]. An
multi-part image segmentation analysis architecture          association rule mining is also being used with decision
combines algorithms for three levels which are briefly       tree structure [10] for image classification. Automatic
described under section 3. By the way, the user can          classification system [11] is applied mainly for medical
extract description of rare multimedia process and utilize   image segmentation. But this classification system
it, backed up by a knowledge repository.                     consumes more time and the efficiency is also being less.
                                                             Using similarity fusion and classifiers, the integration of
2. LITERATURE REVIEW                                         segmentation and classification is achieved and presented
                                                             in [12]. In this work, we are going to integrate the
Texture feature is a technique about association between     segmentation and classification process of the given
the pixels in restricted area, dazzling the amends of        natural and aerial images in the form of texture
image whole gray levels. The paper [1] presented a           primitives.
texture feature taking out system supported on local
average binary gray point dissimilarity co-occurrence
matrix, which shared the texture structural study method
                                                             3. PROPOSED INTEGRATING IMAGE
with geometric method. Initially, we determine the           SEGMENTATION AND CLASSIFICATION
standard binary gray level distinction of eight-neighbors    FOR NATURAL AND AERIAL IMAGES
of a pixel to obtain the standard binary gray point          The proposed work is efficiently designed for integrating
distinction image which conveys the deviation prototype      the image segmentation and classification using texture
of the provincial gray levels.                               primitives. The proposed integrating image segmentation
A key characteristic of semantic image segmentation is to    and classification for natural and aerial images is
incorporate restricted and universal features for the        processed under three different phases. The first phase
calculation of limited segment labels. The paper             describes the process of image segmentation using
presented an approach [2] to multi-class segmentation        multitude texture local drawing out process. After
which unites two techniques for this combination: a          segmentation is done, the classification of image is done
Conditional Random Field (CRF) which pairs to                by the adaptation of texture primitives is described in the
restricted image features and image categorization           second phase. The third phase describes the process of
method judges’ universal features. A technique of region-    extraction of requisite information of the image from the
based image segmentation [3] with mean-shift clustering      user. The architecture diagram of the proposed
algorithm is commenced. This technique initially mines       integrating image segmentation and classification for
texture, color, and position features from every pixel to    natural and aerial images (IISCTP) is shown in fig 3.1.
figure aspect vector by choosing proper color space. Then,
these feature vectors are grouping with mean-shift
clustering algorithm.
To discover the diverse textures in an image, an
undemanding approach is to execute texture dimensions
on a poignant window and disperse scalar features to the
entire image pixels equivalent to window centers. Three
new texture skin textures for image processing are
presented supported on gray level shell and compared in
[4]. Image categorization is the most significant step for
image examination. Classification is a computational
system that divides the images into sets consistent with
their features that mined. Classification acts as a vast
position in the medical image diagnosis. In case of
                                                             Fig 3.1 architecture diagram of the proposed IISCTP
DICOM images [5] it is very dangerous for finest

Volume 1, Issue 4 November - December 2012                                                                    Page 13
   International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
       Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856


From the above figure, it is being noted that the proposed     illustration techniques can be categorized into three
IISCTP is efficiently designed for performing both the         levels, specifically statistical, structural, and multi-
classification and segmentation process for the given          resolution filtering methods. Different regions have
natural and aerial images. The segmentation is process is      different structure features in images. In order to extract
done based on the threshold based technique. Threshold         the whole image texture characteristics, the texture
is a technique where decision is done on restrcited pixel      features of each region should firstly be described. The
information. This is an effective method when the              brightness level at a point in an image is highly
intensity points of the items drop directly slight to the      dependent on the brightness levels of neighboring points
series of points in the background. For local statistics,      unless the image is simply random noise. Consequently, it
lookup table is prepared to use in initial region. Then        is rational to explain the local texture features with the
pixels are clustered that convince detailed homogeneity        gray level amendments of pixels in the neighborhood.
measures. At last it creates a homogeneous section, and        For aerial and natural image restricted threshold
including the adjoining regions, which contain analogous       collection based segmentation practice has been
intensity values.                                              developed (shown in Fig 3.2). Research table reins the
The classification is done using texture primitives.           conserved aerial and natural images that comprises of
Texture primitives (textons) are expressed in the form of      homogeneity and arithmetic values of every pixel. 11 x 11
filter-response space, and texture classes represented by      pane size is used for computation of local statistics. This
frequency histograms of these textons. After image has         choice is     based on the minute harmonized regions,
been segmented based on the class, after that, for every       which are formed by the granularity. The pane size must
class, a tree-structured belief network (TSBN) is              be huge enough for the dimension of homogeneity section
sophisticated, where nodes set apart the analogous image       principle and geometric similarity bound. The parameter
regions, and edges, their geometric dependencies. A            collection of the parallel hop depends on the granularity
specified indefinite texture is classified concerning the      or ruin into the images. The early mounting region
highest posterior allocation of the TSBN. The overall          demonstrates the huge number of copied harmonized
process is described in the flowchart which is shown in        region into the image, which was joined with their
fig 3.2.                                                       neighboring region by merging. The parameters for
                                                               integrating principle count on the elevated regularity
                                                               objects such as over segmentation. This algorithm can be
                                                               employed for fully residential ruin images with competent
                                                               segmentation. The combined regions decrease over
                                                               segmentation devoid of using more leveling into the
                                                               image. The absolute segmentation outcomes demonstrate
                                                               precise standardized regions devoid of implementing
                                                               texture-based analysis.




        Fig 3.2 Flowchart of the proposed IISCTP

  3.1 Multitude regional texture extraction using
Region outliers and segregation
Texture is a visual pattern attribute. It is a possession of
regions, and comprises of sub-patterns which are
connected to the pixel allocation in a region. So texture is
a relative possessions and its description must engage
gray values in a spatial neighborhoods. Texture                        Fig 3.3 Architecture of segmented image

Volume 1, Issue 4 November - December 2012                                                                      Page 14
    International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
       Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856


Image Features                                                    in the stage, while associations among them symbolize
  Consider a finite training set of images                        parent-child geometric dependencies.
S = {s1, s2,…sn} and can be represented by a set D.               The process of classifying the segmented image through
    1                                                             texture primitives are shown in fig 3.3. In the fig 3.3, the
  ( si  D i )   ( RGBi )wherei  1,2,.., n _______ (eqn 1)
    2                                                             texture primitive values are extracted in terms of local
          Where λ denotes the intensity ranges and άi             height, shade and indication which retorts over a set of
denotes the alpha distribution and (RGB)i gives the RGB           images with diplomat perspectives and illumination.
value for a single image                                          After extracting the texture primitives, the classification
Texture Extraction                                                of image is done by constructing TSBN. The shaded parts
Texture extraction is done in two steps using the set D           of the classified image indicate the observable parts of the
and summing it up. It is learned using S = {s1, s2, …,            image and white node symbolizes the hidden variables of
sn}. Where Si, i = 1, 2 …m are the patches extracted with         the classified image.
size
d * d . from texture images in training set.
Sum up for all the coefficients for particular texture
                      n
           T ( S )   ( i ) _______________ (eqn 2)
                     i 1
Region Outlier & Segregation
                    x
  Outlierrate        * 100     ______________ (eqn 3)
                    n
Where x denotes the number of outliers found and n
denotes the total number of training images.
Multitudes of Texture Pixels
Texture coordinate Cά between two pixels C0 and C1 is
given by
   C  (1   )C 0  C1 _____________ (eqn 4)
Using the above formulations, the process of the image               Fig 3.4 Process of classification of segmented image
segmentation is done efficiently. Our multitude region-                                     using TSBN
based method usually works as follows: the training               The TSBN is entirely expressed by its joint allocation of
image is further divided into regions by way of grouping          hidden, A= {ai}, and observable, B = {bi} random
the neighboring pixels that has the similar intensity             variables, ∀i∈T , where i specifies a node in the
ranges. The regions that are adjacent to the pixels are           segmentation tree T . A hidden variable, ai, specifies the
then merged based on some criterion such as the RGB               marker of a texture primitive. The marker of node i is
value or the alpha distribution value obtained. Texture           hardened on the marker of its parent j, and is expressed
extraction is the identification of regions based on their        by the conditional prospect, P (ai |aj). The combined
texture. We have also shown that the texture extracted            possibility of A of a specified texture class T is expressed
can also be used for certain tasks such as region                 as
segregation and region outliers. The texture extracted in            P ( A | T , V , I )   i , jT P (a i | a j , T , V , I ) ………
applied for multitude of texture pixels with the function
                                                                                          eqn 5
Multi Texture. The resultant image obtained is the
segmented image.                                                  Where for the roots, we use priors P ( ai | T , V , I ) . As
                                                                  the observables bi is provisionally self-sufficient specified
3.2 Segmented image classification using texture                  the analogous ai, the joint possibility of B can be
primitives                                                        expressed as
In this paper, texture class T is represented with a TSBN            P( B | A, T , V , I )   iT P(bi | a i  k , T , V , I ) …
(tree-structured belief network) identified over viewpoints
                                                                                            eqn 6
V and illumination I. TSBNs are geometric
representations in computer vision and image processing           Where P (bi | a i  k ) is represented as the Gaussian
[13]. The TSBN of an image comprises of concealed and             distribution with constraints programmed in the texture
evident random variables planned in the similar structure         primitive πk. It pursues that the TSBN for texture T and
as that of the analogous segmentation tree of the image.          imaging parameters V and I is exemplified by
Observables are the characteristic vectors of statistical          P ( A, B | T , V , I )   i , jT P (bi | ai ) P (a i | a j ) .. eqn
and photometric properties of the analogous regions in
the segmentation tree, and are commonly self-sufficient                                        7
specified their consequent concealed variables. Hidden            The constraints of likelihoods P (yi|xi, ·) can be processed
variables are specified as the texture primitives, expressed      by using the clusters {Ck} K, obtained in the stage. Next,

Volume 1, Issue 4 November - December 2012                                                                                   Page 15
   International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
       Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856


the evolution prospects, P (ai|aj), can be processed by the     On the image categorization step, two different processes
usual belief propagation algorithm.                             were employed, as declared former. On the primary
Note that in the above formulation the number of models         process, non-supervised classification, the algorithm has
per class is the similar as the number of training images       as measure the Mahalanobis distance that is the similar as
that diverge in V and I constraints. For the principles of      lowest amount distance, except for the covariance matrix.
texture classification, this symbolizes a modeling              From the segmented image of the previous step, it
redundancy, since even substantial distinctions in texture      attempts to discover regions that are analogous. The
emergence of one class might not decrease the                   texture primitives are used here for classifying the
classification accuracy if the other classes are sufficiently   segmented image. The subsequent process, supervised
unusual from it. To decrease the number of                      classification, utilized some samples of the outcome
representations per texture class, utilize the customary K-     acquired in the previous classification process as training
Mediod algorithm. In particular, the set of P (A|T, V, I)       fields.
values, over all texture classes T and parameters V and I,      The results obtained by the use of this methodology were
might be grouped by the K-Medoid into M clusters, and           compared with the classification of segmented images for
characterized by M cluster centers. The update rule of the      analysis using hybrid methodology and Multitude
K-Medoid constantly progresses the cluster center to the        Regional Texture Extraction for Image Segmentation
adjacent data point in the group, but does not incorporate      (MRTE). The percentage of concordance of accurate clear
over the points as the K-Means algorithm. Indeed, in the        classified image parts among the proposed integrating
K-Medoid, the cluster midpoints are forever data points         image segmentationa nd classification using texture
themselves. Consequently, a preferred cluster center can        primitives was 80%. This strengthens the authority of the
be exclusively denied as an individual P(A|T,V,I) point,        proposed      integrating    image     segmentation     and
which, in turn, decides the most diplomat TSBN model            classification using texture primitives [IISCTP].
with V and I values. Remind that the delineated process
processes a diverse number of diplomat models per               5. RESULTS AND DISCUSSION
texture class.
                                                                In this work, we have seen how efficiently we classified
Thus, in the classification stage, a given natural
                                                                the segmented image using texture primitives by
or aerial image is first segmented to attain B values, then
                                                                following the steps described briefly under section 3. An
P (A, B |T, V, I) values are determined using the belief
                                                                experimental evaluation is carried over with the set of
propagation for all M representative representations of all
                                                                natural and aerial images to estimate the performance of
consistency classes, and, finally, the image is classified.
                                                                the proposed integrating image segmentationa nd
                                                                classification using texture primitives and analyzed the
4. EXPERIMENTAL EVALUATION                                      outcomes. The performance of the proposed integrating
The experimentation conducted on Natural images to              image segmentationa and classification using texture
evaluate the performance of proposed integrating image          primitives is measured in terms of
segmentationa nd classification using texture primitives.                i) Performance
Implementation of the proposed algorithm is done in                      ii) classification accuracy
MATLAB. In addition to noise removal, the proposed                       iii) Time consumption
model also present qualitative results for the texture                   iv) Recognition rate
extraction of the natural image edges. The localization                 The below table and graph describes the effects of
accuracy of natural surface detection technique and             the proposed integrating image segmentationa nd
evaluating the precision of determining virtual inner           classification using texture primitives and compared the
layers of separation is a clinically relevant task for which    results with an existing classification of segmented
the system uses 2D imaging.                                     images for analysis using hybrid methodology and
The datasets used here are segmented homogeneous                Multitude Regional Texture Extraction for Image
region, number of images and number of pixels in                Segmentation (MRTE).
segmented region. The experimentation presented here                   Table 5.1 No. of pixels vs. performance rate
gives a specify homogeneity criteria and produce the
homogeneous region, and merging the neighboring                                        Performance rate (%)
                                                                 No. of
regions, which have similar intensity values.                    Pixels    Proposed     Hybrid              Existing
                                                                                                 MRTE
During the segmentation step, much estimation was                           IISCTP     approach             SRGM
completed: degree of resemblance of 15, 20, 30 and 35,             100         56         50        48        25
and minimum area of 40 and 50 pixels. The                          200         68         59        54        34
disintegration level measured sufficient to the lessons            300         75         68        61        45
was, degree of resemblance 40 and minimum area of 45               400         84         75        70        56
pixels. After description of these thresholds, a segmented         500         90         82        78        60
image was formed to ensure if the disintegration level was
sufficient or not to the balance used and the authenticity.

Volume 1, Issue 4 November - December 2012                                                                       Page 16
                      International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
       Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856


The above table (table 5.1) describes the performance rate                                                                                                       20                  78            68             59           42
of the segmentation of the given natural and aerial                                                                                                              25                  83            75             64           50
images. The segmentation accuracy of the proposed
integrating image segmentationa nd classification using                                                              The above table (table 5.2) describes the classification
texture primitives is comapred with the previous works                                                               accuracy of the segmented parts of the given natural and
hybrid methodology, Multitude Regional Texture                                                                       aerial images. The classification accuracy of the proposed
Extraction for Image Segmentation (MRTE) and existing                                                                integrating image segmentationa nd classification using
seeded region growing model (SRGM).                                                                                  texture primitives is comapred with the previous works
                                                                                                                     hybrid methodology, Multitude Regional Texture
                                    100                                                                              Extraction for Image Segmentation (MRTE) and existing
  P e r fo r m a n c e ra te (% )




                                     80                                                                              seeded region growing model (SRGM).




                                                                                                                       C la ss ifc ation ac cu rac y (% )
                                     60                                                                                                                     100

                                     40                                                                                                                      80

                                     20                                                                                                                      60

                                     0                                                                                                                       40
                                          0           100       200         300          400       500         600                                           20
                                                                       No. of pixels
                                                                                                                                                                0
                                                                                                                                                                    0            5        10            15         20         25          30
                                              Proposed IISCTP   Hybrid approach        MRTE    existing SRGM
                                                                                                                                                                                                No. of features

                                              Fig 5.1 No. of pixels vs. performance rate                                                                                Proposed IISCTP   Hybrid approach     MRTE       existing SRGM


Fig 5.1 describes the performance rate of the                                                                                                               Fig 5.2 No. of features vs. classification accuracy
segmentation of the given natural and aerial images. The
performance of the scheme here is measured as the                                                                    Fig 5.2 describes the classification accuracy of the
average segmentation accuracy across all image based on                                                              segmented parts of the given natural and aerial images.
their pixels. The accuracy a is reviewed using the                                                                   Classification accuracy is measured based on the number
intersection/union metric,                                                                                           of features obtained through image segmentation. Using
                                                         tp                                                          texture primitives, the classification is performed in the
                                          a                                                                         proposed IISCTP. Since the image is already segmented
                                                    tp  fp  fn
                                                                                                                     efficiently in the form of multi-tude regional texture
Where tp, fp, and fn are true positives, false positives, and                                                        extraction method, the classification is done in a reliable
false negative, correspondingly. Based on the number of                                                              manner. Compared to the previous works hybrid
pixels in image, the segmentation of the image is done.                                                              methodology, Multitude Regional Texture Extraction for
Even the number of pixels increases, the segmentation                                                                Image Segmentation (MRTE) and existing seeded region
accuracy in the proposed integrating image segmentation                                                              growing model (SRGM) which concentrates only on the
and classification using texture primitives is high. Since                                                           image segmentation, the proposed integrating image
the proposed IISCTP followed the multitude regional                                                                  segmentation and classification using texture primitives
texture extraction scheme, the segmentation is done absed                                                            performed classification well and accuracy achives 85-
on the extarction of texture feature and it is being                                                                 90%.
processed well. Compared to the previous works hybrid                                                                       Table 5.3 Segmented parts of image vs. time
methodology, Multitude Regional Texture Extraction for                                                                                      consumption
Image Segmentation (MRTE) and existing seeded region
growing model (SRGM) which concentrates only on the
image segmentation, the proposed integrating image                                                                    Segmen                                                         Time consumption (seconds)
segmentationa nd classification using texture primitives                                                                ted                                                             Proposed
                                                                                                                      parts of                                              Proposed                            Existing
performed segmentation well and accuracy achives 90%.                                                                                                                        IISCTP
                                                                                                                                                                                         Hybrid     MRTE
                                                                                                                                                                                                                SRGM
                                                                                                                       image                                                            approach
            Table 5.2 No. of features vs. classification accuracy                                                                                           5                  4.3         5.8         7         10.2
                                                                                                                                                            10                   5.8             6.9              8.6              13.5
                                                             Classification accuracy (%)                                                                    15                   8.4             9.2              10.3             15.8
                        No. of
                       Feature                         Propose     Hybrid              Existin
                                                                                                                                                            20                   9.6            11.2              12.5             17.3
                          s                               d       approac MRTE            g
                                                       IISCTP         h                SRGM                                                                 25                  10.2            13.4              14.8             20.4
                                      5                   45          48         40      25
                                      10                  57          53         48      31                          The above table (table 5.3) describes the consumption of
                                      15                  62          60         51      38                          time to perform the integration of image classification

Volume 1, Issue 4 November - December 2012                                                                                                                                                                                     Page 17
                           International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
       Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856


and segmentation based on the aerial and natural images.                                                                          100




                                                                                                           Recognition rate (%)
The segmentation accuracy of the proposed integrating                                                                              80
image segmentationa nd classification using texture                                                                                60
primitives is comapred with the previous works hybrid                                                                              40
methodology, Multitude Regional Texture Extraction for                                                                             20
Image Segmentation (MRTE) and existing seeded region                                                                               0
growing model (SRGM).                                                                                                                   0          20         40          60         80          100        120        140
                                                                                                                                                                    No. of texture primitives
                           25
  Time consumption (sec)




                                                                                                                                            Proposed IISCTP        Hybrid approach        MRTE         existing SRGM
                           20

                           15

                           10                                                                              Fig 5.4 No. of texture primitives vs. recognition rate
                            5                                                                            Fig 5.4 describes the recognition rate of the given natural
                            0                                                                            and aerial images based on texture primitives. Even the
                                0        5            10             15      20      25             30
                                                                                                         global recognition rate rises as the number of training
                                                      Segmented parts of image
                                                                                                         images per class develops into better, where,
                                    Proposed IISCTP        Hybrid approach   MRTE   existing SRGM        fascinatingly, ours is better than the one stated for a
                                                                                                         training set. This recommends that the proposed IISCTP
 Fig 5.3 Segmented parts of image vs. time consumption                                                   are accomplished by confining more considerable
                                                                                                         information from a few training images. Compared to the
Fig 5.3 describes the consumption of time to perform the                                                 previous works hybrid methodology, Multitude Regional
integration of image classification and segmentation                                                     Texture Extraction for Image Segmentation (MRTE) and
based on the aerial and natural images. The time                                                         existing seeded region growing model (SRGM) which
consumption is measured based on the time taken to                                                       concentrates only on the image segmentation, the
evaluate the segmentation and the time needed to                                                         proposed      integrating   image     segmentation     and
determine the classification. So, the time consumption is                                                classification using texture primitives has high
measured as the total time taken to perform the                                                          recognition rate and the avriance is 80-85% high in the
classification of the given aerial and natural images. The                                               proposed IISCTP.
time consumption is measured in terms of seconds. In the
proposed IISCTP, the consumption of time to perform the                                                  6. CONCLUSION
appropriate operation is less compared to the previous                                                   In this work, a primitive textures approach of
works hybrid methodology, Multitude Regional Texture                                                     segmentation and classification of natural and aerial
Extraction for Image Segmentation (MRTE) and existing                                                    images were presented. First the image is segmented and
seeded region growing model (SRGM).                                                                      then, using non-supervised and supervised classification
Table 5.4 No. of texture primitives vs. recognition rate                                                 techniques, the image is classified was a comparative
                                                                                                         examination among the pixel-per-pixel and the region
   No. of                                                  Recognition rate (%)                          classification. The proposed integrating image
  texture                               Propose            Proposed                                      segmentationa nd classification using texture primitives
 primitive                                                                      Existing
                                           d                Hybrid    MRTE                               consumes less effort by the user to attain the exact
     s                                                                          SRGM
                                        IISCTP             approach                                      classification on homogeneous spectral regions. That’s as
                           25              75                 65         54       25                     there isn’t essential a huge number of samples, saving
                           50                80                 71           60           35             computation time. The areas acquired with the
                           75                82                 75           61           40             segmentation method using region-growing algorithm,
                                                                                                         illustrated enhanced results than the pixel classification.
                           100               86                 79           67           42
                                                                                                         Concluding that, we can judge the universal results
                           125               90                 80           70           48             illustrated by utilizing region classification were
                                                                                                         acceptable. The proposed integrating image segmentation
The above table (table 5.4) describes the recognition rate                                               and classification using texture primitives methodology
of the given natural and aerial images based on texture                                                  provides good prospective to be employed in equally
primitives. The recognition rate of the proposed                                                         activities                                             and
integrating image segmentationa nd classification using                                                  an experimental evaluation showed that the a better
texture primitives is comapred with the previous works                                                   classification results on segmented image.
hybrid methodology, Multitude Regional Texture
Extraction for Image Segmentation (MRTE) and existing
seeded region growing model (SRGM).                                                                      REFERENCES
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   International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
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Volume 1, Issue 4, November – December 2012                                    ISSN 2278-6856


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Description: International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 1, Issue 4, November – December 2012, ISSN 2278-6856, Impact Factor of IJETTCS for year 2012: 2.524