An approach to texture-based image recognition by deconstructing multispectral co-occurrence matrices using Tchebichef orthogonal polynomials

The existing use of summary statistics from co-occurrence matrices of images for texture recognition and classification has inadequacies when dealing with non-uniform and colored texture such as traditional `Batik¿ and `Songket¿ cloth motifs. This study uses the Tchebichef orthogonal polynomial as a...

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Bibliographic Details
Main Authors: Cheong, M., Loke, K.S.
Format: Conference Proceeding
Language:English
Subjects:
Color
Gray-scale
Image databases
Image recognition
Matrix decomposition
Pixel
Polynomials
Shape
Statistics
Testing
Online Access:Request full text
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Summary:The existing use of summary statistics from co-occurrence matrices of images for texture recognition and classification has inadequacies when dealing with non-uniform and colored texture such as traditional `Batik¿ and `Songket¿ cloth motifs. This study uses the Tchebichef orthogonal polynomial as a way to preserve the shape information of cooccurrence matrices generated using the RGB multispectral method; allowing prominent features and shapes of the matrices to be preserved while discarding extraneous information. The decomposition of the six multispectral co-occurrence matrices yields a set of moment coefficients which can be used to quantify the difference between textures. The proposed method, when tested with a subset of the Vision Texture (VisTex) database and a collection of `Batik¿ and `Songket¿ motifs, yielded promising results of 99.5% and 95.28% classification rates respectively using the 3-nearest neighbor classifier.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2008.4761325