Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.patcog.2003.09.010 | Pattern Recognition |
Keywords | Field | DocType |
Color texture,Co-occurrence matrix,Integrative features,Kolmogorov distance,Image classification | Color space,Color balance,RGB color model,Artificial intelligence,Computer vision,Pattern recognition,Color histogram,Image texture,Histogram equalization,Color normalization,Texture filtering,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 5 | 0031-3203 |
Citations | PageRank | References |
95 | 3.31 | 16 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoph Palm | 1 | 101 | 4.54 |