Authors:
Tamiris Negri
1
;
Fang Zhou
2
;
Zoran Obradovic
2
and
Adilson Gonzaga
3
Affiliations:
1
University of São Paulo, Temple University, Federal Institute of Education and Science and Technology of São Paulo, Brazil
;
2
Temple University, United States
;
3
University of São Paulo, Brazil
Keyword(s):
Color Texture, Texture Description, Illumination, Local Descriptors.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
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 robustn
ess of the descriptor and increases the classification accuracy.
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