Color-related Local Binary Pattern: A Learned Local Descriptor for Color Image Recognition

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 feature learning strategy is employed to learn the dominant color-related patterns for reducing the dimension of feature vector and further improving the discriminatively of features. The theoretic analysis show that the proposed RSS can provide more discriminative information, and has higher noise robustness as well as higher illumination variation robustness than traditional RGB space. Experimental results on four groups, totally twelve public color image datasets show that the proposed method outperforms most of the LBP variants for color image recognition in terms of dimension of features, recognition accuracy under noise-free, noisy and illumination variation conditions.