Name
Abstract | ||
|---|---|---|
Classifying color textures under varying illumination sources remains challenging. To address this issue, this paper introduces a new descriptor for color texture classification, which is robust to changes in the scene illumination. The proposed descriptor, named Color Intensity Local Mapped Pattern (CILMP), incorporates relevant information about the color and texture patterns from the image in a multiresolution fashion. The CILMP descriptor explores the color features by comparing the magnitude of the color vectors inside the RGB cube. The proposed descriptor is evaluated on nine experiments over 50,048 images of raw food textures acquired under 46 lighting conditions. The experimental results have shown that CILMP performs better than the state-of-the-art methods, reporting an increase (up to 20.79%) in the classification accuracy, compared to the second-best descriptor. In addition, we concluded from the experimental results that the multiresolution analysis improves the robustness of the descriptor and increases the classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006143403780388 | PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 4 |
Keywords | Field | DocType |
Color Texture, Texture Description, Illumination, Local Descriptors | Computer vision,Pattern recognition,Computer science,Local binary patterns,Color texture,Artificial intelligence | Conference |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tamiris Trevisan Negri | 1 | 3 | 1.04 |
| Fang Zhou | 2 | 12 | 11.81 |
| Zoran Obradovic | 3 | 1110 | 137.41 |
| Adilson Gonzaga | 4 | 80 | 13.27 |