1 / 9

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

chanton
Download Presentation

Fast color texture recognition using chromaticity moments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related