Document Sample

Short Paper Proc. of Int. Conf. on Advances in Computing, Control, and Telecommunication Technologies 2011 Assessing the Use of Similarity Distance Measurement in Shape Recognition Siti Salwa Salleh, Noor Aznimah Abdul Aziz, Daud Mohamad, and Megawati Omar2 Faculty of Computer and Mathematical Science, 2 Research Management Institute University Technology MARA, 40450 Shah Alam, Selangor, Malaysia 1 Email: ssalwa@tmsk.uitm.edu.my Abstract— Distance measure is one of the techniques widely shape recognition recognizes reference shape from other used to measure the similarity between two feature matrices shape, and test whether the reference shape is applicable or of objects. The objective of this paper is to explore researches otherwise [20, 10]. This recognition technique is being used on applied distance measures in shape-based recognition. In in industries such as fashion, architectural, journalism, distance measures computation, patterns that are similar will advertisement, education, and entertainment. have a small distance while uncorrelated pattern in the feature space will have a far a part distance. The search for effective Among recognition techniques, similarity measure using distance measures of shape recognition is always active as distance measure techniques is one of the popular methods. each measure suffers certain drawbacks and it must be selected It works in the manner that small distances corresponding to appropriately to handle chosen shape features of the objects. large similarities and large distances corresponding to the Thus in this paper, the Chord, Cosine, Euclidean, small. This approach is widely used for measuring the Mahalanobis, Trigonometric and Jaccard distance were similarity between objects as reported in [3, 8, 9, 12, 14, 16]. reviewed and discussed in terms of their contributions, Generally, distance measures is extensively employed in measures strengths and weaknesses. It was found that Jaccard content-based image retrieval for shape-based trademark and Mahalanobis have their strengths that they were selected retrieval, shape-based image retrieval, planar object to guide in justifying and identifying appropriate distance measures of our future work on two dimensional sketching recognition such as handwritten character recognition and images. The new distance measure is expected to perform other. Likewise, various similarity measures techniques have better and capable to obtain significance degree of accuracy also been used to define appropriate distance function that and recognition rate for real time recognition for automatic provides the reasonable results for the images comparison. classifier. They are Euclidean Distance [14], Mahalanobis Distance [3], Chord Distance [9] Cosine Distance [16], Trigonometric Index Terms— Distance Measures, shape recognition, Distance [8] Jaccard Distance [12] and others. But among recognition classifier, similarity measures, shape context. those, not all distance measures work well on a sketch-based input and each distance measure have its own strength and I. INTRODUCTION also suffer from certain weakness. Over the years, object recognition has been employed in several approaches. One of the approaches is a shape II. PROBLEM STATEMENT AND OBJECTIVE descriptor which covers shape space [15], chord context [9], A central problem in object recognition is to determine shape context [13], Fourier descriptors [21] and more of its proper distance measure for a particular object. Extensive kind. Shape descriptor has been deployed to find the studies on shape recognition prove some significance corresponding points or features between shapes [13, 1]. progresses but current use of the recognition and shape Previous shape recognition researchers widely use neural classification technique has yet to prove satisfactory. Since network [17, 7], hidden markov model (HMM) [19], simple each distance measure has its own strength and weakness, matching techniques or similarity measures [3, 8, 9, 12, 14, researchers must be careful to choose the different ways of 16]. However, recognition using Neural Network and HMM measuring the distance, which is the chosen approaches requires a large set of training database [18, 3], while simple must conform to their needs and applications. It is important matching technique does not return a promising recognition to note that, there is no general distance measure that works performance. Moreover, in certain application, large dataset best for every kind of shape feature. Hence, the best way in are not available, neither nor it practical. Shape is a dominant selecting distance or similarity measure is by identifying and feature of an object as it consist lines, contours, curves, and considering the most minimum vector space values by the vertices and it is normally presented by discrete set of points use of simple function [8]. or set of pixels value sampled from region or internal and Another problem we may bear in mind is that however external contour on the object [1, 5]. Generally, there are few appropriate the distance function is, it still suffers from some recognition techniques that based on object properties of kind of shortcomings. A survey by Wang et. al [14] men- [5,6,10] which are shape, texture, color, and brightness. But tioned that most of existing distance function encompasses compared to other features, shape is the most unique where a complexity of the computation measure. It also brings some it is able to recognize objects more efficiently. Practically, difficulty to combine or embed the metric proposed with the 165 © 2011 ACEEE DOI: 02.ACT.2011.03. 19 Short Paper Proc. of Int. Conf. on Advances in Computing, Control, and Telecommunication Technologies 2011 powerful classifier. Therefore, this study will identify a dis- First of all, Euclidean distance is the most commonly used tance measure that fulfils the following criteria (listed based in similarity measurement for its simplicity [8, 14] where it on priority): i) A measure that works well on strokes which computes the difference of points in magnitude and widely work well on sketching; Invariant to object transformation; applied in several applications such as character recognition, ii) A measure that can be modified and added with guiding face recognition, image retrieval and others. On the other elements; iii) Low computation suitable for real time (auto- hand, the images in Euclidean distance are not necessarily matic) checking purpose. similar in all features. To overcome the shortcoming in Euclidean, Zou and Umugwaneza [16] implemented a distance III. ANALYSIS OF RECENT RESEARCH metrics Cosine distance to improve the formulation by multiplying the first and second coordinates and add the The analysis was conducted by way of examining the results from Euclidean distance dimensional profile data strengths and shortcomings of each measure. To iterate, matrix. works that applied the distance functions are generally used Another measure that contains a simple distance function in the area of image retrieval, shape retrieval and handwritten and requires minimum computation time is Jaccard Distance. character recognition. They commonly focus on overcoming In this sense, Jaccard Distance is better that it outdoes overcome certain problems such as computation time, Euclidean Distance in shape recognition. Jaccard Distance is invariant of image transforms, computational complexity. basically employed to compare two objects in a binary format Secondly, the focus is to increase its retrieval accuracy. In and it also does not require a large set of data for training and this paper, the most recent studies that applied five different testing purposes. It works by measuring the asymmetric distance functions were studied. The studies are conducted information on binary variables, the comparison between two by (i) Wang et. al [14] who presented new Euclidean distance vector components. However, despite its strength and being for images, called IMED which image metric can be embedded used widely, Jaccard Distance’s disadvantage is its variant in the existing image recognition methods for 2D image. ii) to image transformations. This distance function is commonly Nemmour and Chibani [12] who proposed new kernel for the applied on binary data where the similarity computation support vector machine for handwritten digit recognition computes the values of 0 and 1. The input object must be in based on the Jaccard negative distance. The computation of binary form and therefore, Jaccard’s method cannot be used Jaccard distance in their study determines the correlated or if any transformations are employed to the object features. uncorrelated pattern based on pixel-based description where Another requirement is that the size of both binary object it takes into account the number of pixels in foreground and and shapes must be of similar size. But Jaccard’s limitation background for both patterns that leads to similar or dissimilar can be improved by adding a pre-processing taks prior to the patterns. (iii) Chen et. al [3] comes out with the Progressive computation. Mahalanobis distance for financial hand-written Chinese At present, the Mahalanobis distance is one of the character recognition. Mahalanobis distance derived by a commonly distance measures used in CBIR. However, its probability density function of multivariate normal distribution weakness is that Mahalanobis distance’s computation needs or the key is the calculation of covariance matrix. (iv) Zou sufficient training samples and long computation time in and Umugwaneza [16] that proposed cosine distance function recognition. Computation time is one of the important for shape-based trademark retrieval for 2D images. (vi) distributions in character recognition and retrieval Mingqiang et. al [9] who proposed shape descriptor based applications, therefore consuming long computation time will on chord context use to measuring similarities between 2D affect the recognition performance. Consequently, two shapes And finally (vii) Li et. al [8] who proposed new Mahalanobis distance function will be more time-consuming similarity measurement method, an algorithm for object shape on large database. Eventhough Chen et al. [3] conducted a analysis based on Trigonometric distance. research to reduce the computation time of original Mahalanobis Distance by creating a Progressive IV. DISCUSSION Mahalanobis Distance to reduce computational loads at The performance of recognition effectiveness of different satisfactory level, its computation time still consumes more measures investigated theoretically based on the work time than the Euclidean Distance. But interestingly, reported by researchers. We studied works conducted by Mahalanobis [3] performs reliable result on financial hand- previous researchers who did some improvement on the written Chinese characters that involve variations of size and following distance function: Chord Distance, Cosine Distance, density; stroke translation; stroke length and width; broken Euclidean Distance, Mahalanobis Distance, Trigonometric and connected stroke and others. These applicable methods distance and Jaccard Distance. The objects that these contribute to the accurate automatic retrieving system in researchers work with were in many forms such as trademark images [16] handwritten digit recognition [12] photography, hand sketches, drawing and others. The automatic bank cheque processing [3], and texture (Chen and researches analyzed were chosen due to their closest input Chu, 2005). Another distance is a Cosine Distance of which type with our intended research, having a grayscale format as whose strength is its consideration of the correlation of the the grayscale format can be computed in minimal time. features vector. This measure normalizes all feature vectors Moreover, most of the input used contains strokes and tiny to unit length, comparing the angle between two shape lines. features vectors. In [16] worked on trademark retrieval using 166 © 2011 ACEEE DOI: 02.ACT.2011.03.19 Short Paper Proc. of Int. Conf. on Advances in Computing, Control, and Telecommunication Technologies 2011 Cosine Distance function where it shows a weakness where However, in application that receives direct input using a this measure is invariant with the scaling of the image content pen input device faces some disturbances in recognizing the and treating similar images in different ways. However so, the object, where the pen device will show differences from the Cosine Distance produces more accurate results compared to pen pressures. Different pressure between individuals [4, 11] the Euclidean Distance in retrieval effectiveness. produces significance differences in the result of recognition. Chord Distance also shows a high degree of retrieval The summary of previous work is shown below (Table 1). accuracy than the other measures on the MPEG-7 CE-1 TABLE I. database and the Kimia silhouettes datasets retrieval test. EVOLUTION OF DISTANCE FUNCTIONS The strength here is that it is robust to noise and occultation. Another potency is that its capability to describe a frequency distribution of chord lengths with different orientations. Shape descriptor using Chord context [9] for shape description in CBIR showed that the measure is invariant in image translation, rotation and scaling. However, the propose descriptor perform no special operations to resist affine transformation. Furthermore, in most researches on chord distance, it also provides poor recognition result if the input consists of strokes and lines. Likewise, Trigonometric Distance applied in the object shapes analysis for retrieval application produced better recognition rate on similarity images in different angles. Li et. al [8] uses Trigonometric distances that normalize the distance of two points in image similarity. Trigonometric’s forte is that it reduces the influence of noise and produces better recognition rate than Euclidean distance in image retrieval. However, it requires a well cleaned input whereby any noise can easily affect the recognition performance. Defining and filtering noises in real time application require efficient noise removal algorithm and involve a careful choice of feature extractions steps and which may be involve another difficult study. Hence in a nutshell, it was found that that each distance function posses its own strength weaknesses in the context of strokes and line input as well as for a real time application. In short, sigh sensitivity through small deformation may result in a large Euclidean Distance. The individual distance needs normalized distance to decrease the percentage of CONCLUSIONS missed images in the results for effective image retrieval [16]. Comparison was made on the distance measures and While the Mahalanobis Distance is time consuming when basically, each distance performs differently against common tested on large database, and also, limited samples cause a essential properties such as noise resistance, affine bad influence. Other distance functions do not resist affine invariance, occultation invariance, statistical independent, transform, contributing to a major weakness in accuracy reliability, translation, rotation, scale invariance and others. retrieved, for example, Chord Distance [9]. Considerable efforts Most of the distance functions mentioned above require basic have been made to obtain intuitively reasonable. Although pre processing tasks on the noise removal, object scale and there is a remaining setback, some methods have been evolved rotation which are important in recognizing shapes. In for better achievement in recognition retrieval. The proposed conclusion, we chose Jaccard and Mahalanobis to be the similarity measure using distance function contribute to main references for our future work. Jaccard outshines the strengths in flexibility against handling various type of queries others by its simplicity but able to maintain higher accuracy. and robust to noise and minor occultation. On the other hand, Mahalanobis is able to deal with strokes Therefore, the Chord Distance, Cosine, Jaccard, and lines which provide us likelihood to produce a more Mahalanobis and Trigonometry are found to be better than promising equation that combines both distance equations. the Euclidean distance in all courses. Unfortunately, Again, in spite of its simplicity that allows minimum Euclidean Distance’s hitches are on scale properties where it computation time, Jaccard works efficiently on a binary image produces far distances; no similarity although the objects and Mahalanobis’s invariant to object transform works best are in the normalized scale images and computated base for real time application which will, in turn, perform automatic magnitude. Therefore, Euclidean suffers from the most checking on the object. Hence, our future work will focus on important drawback that makes it offer the lowest degree of two dimensional sketching inputs from pen device. This may recognition. On the other hand, Cosine Distance works well improve one of the selected distance functions and may com- on the objects without rotation, translation and scale. bine it with other distance functions to improve similarity 167 © 2011 ACEEE DOI: 02.ACT.2011.03.19 Short Paper Proc. of Int. Conf. on Advances in Computing, Control, and Telecommunication Technologies 2011 measures performances. It deems that new distance function [9] Y. Mingqiang, K. Kidiyo, and R. Joseph, “Shape Matching will perform better on sketching images where it will be able and Object Recognition Using Chord Contexts”, Proceedings of the to obtain better significance degree of accuracy and recogni- International Conference on Visualisation,Washington, DC, USA, tion rate for real time recognition for automatic classifier. pp. 63-69, 9-11 July 2008. [10] Y. Mingqiang, K. Kidiyo, and R. Joseph, “A Survey of Shape Feature Extraction Techniques”, Pattern Recognition Techniques, ACKNOWLEDGMENT Technology and Applications, pp.43-90, 2008. This work was supported by the FRGS Grant, Ministry of [11] M. Nakai, T. Sudo, H. Shimodaira, and S. Sagayama, “Pen Higher Education, Malaysia (600-RMI/ST/FRGS 5/3 Fst (98/ Pressure Features for Writer Independent On-Line Handwritting Recognition Based on Substroke HMM”, Proceedings of the 16th 2010) Internaional Conference on Pattern Recognition, pp. 220-223, 2002. [12] H. Nemmour, and Y. Chibani, “New Jaccard-Distance Based REFERENCES Support Vector Machine Kernel for Handwritten Digit [1] S. Belongie, G. Mori, and J. Malik, “Shape Matching and Recognition”, Proceedings of the 3rd International Conference on Object Recognition Using Shape Contexts”, IEEE Transaction on Information and Communication Technologies: From Theory to Pattern Analysis and Machine Intelligence, vol.24, pp. 509-522, Application, pp. 1-4, 7-11 April 2008. April 2002. [13] L.B. Singh, and S.M. Hazarika, “Enhanced Shape Context for [2] C.C. Chen, and H.T. Chu, “Similarity Measurement between Object Recognition”, Proceedings of the 15th International Images”, Proceedings of the 29th Annual International Computer Conference on Advanced Computing and Communication, Software and Applications Conference, Washington, DC, USA, pp. Washington, DC, USA, pp. 529-534, 18-21 December 2007. 41-42, 26-28 July 2005. [14] L. Wang, Y. Zhang, and J. Feng, “On the Euclidean Distance of [3] G. Chen, H.G. Zhang, and J. Guo, “Efficient Computation of Images”, IEEE Transactions on Pattern Analysis and Machine Mahalanobis Distance in Financial Efficient Hand-Written Chinese Intelligence, vol.27, pp. 1334-1339, 2005. Character Recognition”, Proceedings of the Sixth International [15] J. Zhang, X. Zhang, H. Krim, and G. G. Walter, “Object Conference on Machine Learning and Cybernetics, Hong Kong, representation and recognition in shape spaces”, Pattern pp. 2198-220, 19-22 August 2007. Recognition, vol.36, pp. 1143-1154, May 2003. [4] N.N. Daeid, L. Whitehead, M. Allen, “Examining the effects [16] B.J. Zou, and M.P. Umugwaneza, “Shape-based Trademark of paper type, pen type, writing pressure and angle of intersection Retrieval using Cosine Distance Method”, Proceedings of the Eight on white and dark dominance in ESDA impressions of sequenced International Conference on Intelligent Systems Design and strokes—An application of the likelihood ratio”, Forensic Science Application, Washington, DC, USA, pp. 498- 504, 26-28 November International, vol.181, pp. 32-35, October 2008. 2008. [5] M.D. Daliri, and V. Torre, “Robust symbolic representation [17] J.K. Basu, D., Bhattacharyya, and T.H. Kim, “Use of Artificial for shape recognition and retrieval”, Pattern Recognition, vol. 41, Neural Network in Pattern Recognition”, Software Engineering pp. 1782 – 1798, 2007. and Its Application, vol.4, pp. 23-34, 2010. [6] S. J. Dickinson, Object Representation and Recognition, E. [18] M., Bicego, U. Castellani, and V. Murino, “A Hidden Markov Lepore and Z. Pylyshyn (eds.) Rutgers University Lectures on Model approach for appearance-based 3D object recognition”, Cognitive Science, Basil Blackwell publishers, pp 172–207, 1999. Pattern Recognition Letters, vol.26, pp. 2588-2599, 2005. [7] J.X. Du, D.S. Huang, X.F. Wang, and X. Gu, “Shape recognition [19] J. Alkhateeb, J. Ren, J. Jiang, and H. Al-Muhtaseb, “Offline based on neural networks trained by differential evolution handwritten Arabic cursive text recognition using Hidden Markov algorithm”, Neurocomputing, vol.70, pp. 896-903, January 2007. Models and re-ranking.”, Journal of Pattern Recognition Letters, [8] Z. Li, K. Houl, and H. Li, “Similarity Measurement Based on vol.32, pp. 1081-1088, 2011. Trigonometric Function Distance”, Proceedings of the 1st [20] E. Attalla, and P. Siy, “Robust shape similarity retrieval based International Symposium on Pervasive Computing and on contour segmentation polygonal multiresolution and elastic Applications, pp. 227-231, 3-5 August 2006. matching”, Pattern Recognition, vol.38, pp. 2229-2241, 2005. [21] D. Zhang, and G. Lu, “Shape-based image retrieval using generic Fourier descriptor”, Signal Processing: Image Communication, vol.17, pp. 825-848, 2002. 168 © 2011 ACEEE DOI: 02.ACT.2011.03.19

DOCUMENT INFO

Shared By:

Categories:

Tags:

Stats:

views: | 34 |

posted: | 5/18/2012 |

language: | English |

pages: | 4 |

How are you planning on using Docstoc?
BUSINESS
PERSONAL

By registering with docstoc.com you agree to our
privacy policy and
terms of service, and to receive content and offer notifications.

Docstoc is the premier online destination to start and grow small businesses. It hosts the best quality and widest selection of professional documents (over 20 million) and resources including expert videos, articles and productivity tools to make every small business better.

Search or Browse for any specific document or resource you need for your business. Or explore our curated resources for Starting a Business, Growing a Business or for Professional Development.

Feel free to Contact Us with any questions you might have.