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					Fast color texture
recognition using
chromaticity moments
         Pattern Recognition Letters 21 (2000) 837-841




    Presented by Waseem Khatri
Existing approaches to texture analysis
   Statistical – Moments , Co-occurrence matrix
   Model Based – Fractal, Stochastic models
   Structural – Microtexture , Macrotexture , Morphology
   Transform – Fourier , Wavelet , Gabor transforms

Limitations
   Computationally Intensive
   Cannot differentiate subtle variation in textures
   Scaling and Rotation
               Proposed Method



   CIE xy chromaticity diagram of an image
   2D and 3D moments to characterize a given color
    image.
   Classification using distance measure
              CIE XYZ Color Space
Chromaticity:
    The quality of a color as
    determined by its dominant
    wavelength


   Chromaticity diagram is a
    two dimensional
    representation of an image
    where each pixel produces a
    pair of (x,y) values
   Matlab: rgb2xyz
2D Shape and 2D distribution




 2D Trace            2D Distribution
                     Moments
Definition:
  If f(x,y) is piecewise continuous and has non zero values only
  in a finite part of the xy-plane, moments of all orders exist and
  the moment sequence (mpq) is uniquely determined by f(x,y).
                           
              m pq      
                         
                                x p y q f ( x, y ) dxdy


 Why moments ?
 Moments uniquely capture the nature of both the 2D shape and
 the 2D distribution of chromaticities.
                           Procedure
   Given image is converted into CIE xyz color space
   The trace of the chromaticity diagram is computed
         T(x,y) = 1    if exists (i,j) : I(i,j) produces (x,y)
                  0    otherwise;
                 0<i<Lx , 0<i<Ly


   The 2D distribution is computed using:
         D(x,y)= k , where k= #pixels producing (x,y)

   Moments are computed using:
                          X s 1Ys 1
        M T (m, l )       
                           x 0 y 0
                                        x m y l T ( x, y)

                         X s 1Ys 1
        M D (m, l )       x
                          x 0 y 0
                                        m
                                            y l D( x, y)
                   Classification

   Moments for all the classes in the database are
    computed
   Moments for the test sample is computed
   Minimum Distance measure
            d=|x-x | where x is the feature vector of the class
                        i

                                  xi is the feature vector of the test image

   The given test sample is assigned to the class
    from which it has the minimum distance
               Conclusion
Advantages
 Simple

 Efficient

 Effective for a database with distinct
  texture
 Uses small number of chromaticity
  moment features

Drawbacks
 Error rate is high if the database contains
  textures that are not significantly different

				
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