Color-related Local Binary Pattern: A Learned Local Descriptor for Color Image Recognition
Bin Xiao, Tao Geng, Xiuli Bi, Weisheng Li

TL;DR
This paper introduces a novel color-related local binary pattern (cLBP) descriptor that leverages a relative similarity space to improve color image recognition by capturing dominant patterns and reducing feature dimensionality.
Contribution
The paper proposes a new cLBP method utilizing a relative similarity space and feature learning to enhance discriminative power and robustness in color image recognition.
Findings
Outperforms most LBP variants in recognition accuracy
Provides more discriminative information than traditional RGB space
Shows higher robustness to noise and illumination changes
Abstract
Local binary pattern (LBP) as a kind of local feature has shown its simplicity, easy implementation and strong discriminating power in image recognition. Although some LBP variants are specifically investigated for color image recognition, the color information of images is not adequately considered and the curse of dimensionality in classification is easily caused in these methods. In this paper, a color-related local binary pattern (cLBP) which learns the dominant patterns from the decoded LBP is proposed for color images recognition. This paper first proposes a relative similarity space (RSS) that represents the color similarity between image channels for describing a color image. Then, the decoded LBP which can mine the correlation information between the LBP feature maps correspond to each color channel of RSS traditional RGB spaces, is employed for feature extraction. Finally, a…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification
