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Fast color texture recognition using chromaticity moments

Fast color texture recognition using chromaticity moments. Pattern Recognition Letters 21 (2000) 837-841. Presented by Waseem Khatri. Statistical – Moments , Co-occurrence matrix Model Based – Fractal, Stochastic models Structural – Microtexture , Macrotexture , Morphology

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Fast color texture recognition using chromaticity moments

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  1. Fast color texture recognition using chromaticity moments Pattern Recognition Letters 21 (2000) 837-841 Presented by Waseem Khatri

  2. Statistical– Moments , Co-occurrence matrix Model Based – Fractal, Stochastic models Structural – Microtexture , Macrotexture , Morphology Transform – Fourier , Wavelet , Gabor transforms Computationally Intensive Cannot differentiate subtle variation in textures Scaling and Rotation Existing approaches to texture analysis Limitations

  3. Proposed Method • CIE xy chromaticity diagram of an image • 2D and 3D moments to characterize a given color image. • Classification using distance measure

  4. 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

  5. 2D Shape and 2D distribution 2D Trace 2D Distribution

  6. 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). Why moments ? Moments uniquely capture the nature of both the 2D shape and the 2D distribution of chromaticities.

  7. Procedure • Given image is converted into CIE xyz color space • The trace of the chromaticity diagram is computed • The 2D distribution is computed using: D(x,y)= k , where k= #pixels producing (x,y) • Moments are computed using: • T(x,y) = 1 if exists (i,j) : I(i,j) produces (x,y) 0 otherwise; • 0<i<Lx , 0<i<Ly

  8. Classification • Moments for all the classes in the database are computed • Moments for the test sample is computed • Minimum Distance measure d=|x-xi| where x is the feature vector of the class 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

  9. 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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