Alternative to colour feature classification using colour contrast ocurrence matrix
Rosalinda Martinez, Noël Richard, C. Fernandez · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Texture discrimination was the second more important task studied after colour perception and characterization. Nevertheless, colour texture assessment and characterization was few studied and no vector processing was proposed to assess this important visual information. In this work we show the construction of a new vector that integrates fully the information of texture and color. This vector is based on Julesz psico-physics conjectures and the Haralick cooccurrence matrix. A colour texture image in the CIEL*a* b* colour space is characterizing in a 3D matrix, from which it is possible to visually some variations in chromaticity. The performance of this vector had evaluated in tasks of classification in front of other developments that mix the texture and colour information. The colour contrast occurrence matrix (C2O) has the best classification rates in three of the four image database evaluated as OUTEX, VISTEX, STEX and ALOT. C2O texture classification was evaluated in front of co-occurrence matrix (GLMC), run-length matrix (RLM) and local binary patterns (LBP) approaches.