Color Texture Classification by Integrative Co-Occurrence Matrices

  • Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20% and 32% for the first and second baseline, respectively.

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Metadaten
Author:Christoph PalmORCiDGND
DOI:https://doi.org/10.1016/j.patcog.2003.09.010
Parent Title (English):Pattern Recognition
Document Type:Article
Language:English
Year of first Publication:2004
Release Date:2020/05/13
Tag:Co-occurrence matrix; Color texture; Image classification; Integrative features; KolmogKorov distance
Volume:37
Issue:5
First Page:965
Last Page:976
Institutes:Fakult├Ąt Informatik und Mathematik
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
Publication:Externe Publikationen