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Local difference ternary sequences descriptor based on unsupervised min redundancy mutual information feature selection

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Abstract

Texture feature description research have received significant attention in recent years. It is widely known that the local texture feature descriptor can achieve good performance under various image conditions, such as geometric size variation, different poses, complex illumination and partial occlusion. Although Local Difference Binary is an acknowledged excellent feature descriptor, it only computes the intensity and gradient difference on pairwise grid cells and ignores the image grid texture intensity and gradient. This paper proposes a novel local texture descriptor, named as Local Difference Ternary (LDT), which can not only represent difference and texture information of the grid cells intensity and gradient simultaneously, but also capture richer detailed texture information. In addition, the Unsupervised Min Redundancy Mutual Information (UMRMI) for feature selection is proposed to select the optimal subset of LDT features for achieving more powerfully discriminative ability. For the purpose of further improving the efficiency and effectiveness of UMRMI, we extend UMRMI to k-means space, namely k-UMRMI. Furthermore, a multi-degree scheme is adopted to achieve richer texture description. Finally, Radial Function Neural Network is employed for classification, which is an excellent classifier, especially for larger samples. Several experimental results on certain benchmark face databases demonstrate that our proposed method is remarkably superior to some other state-of-the-art approaches under various image conditions.

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Acknowledgements

This project is supported by Fundamental Research Funds for the Central Universities of Ministry of Education of China (310824172001, 310824171008), the National Natural Science Foundation of China (61302150, 61703054), Postdoctoral Science Foundation of China (2014M562356).

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Authors and Affiliations

  1. School of Information Engineering, Chang’an University, Xi’an, 710064, China

    Gao Tao, Zhanwen Liu, Jinpei Cao & Shan Liang

  2. College of Engineering and Computer Science, Australian National University, Canberra, 2601, Australia

    Gao Tao

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  1. Gao Tao
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  2. Zhanwen Liu
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  3. Jinpei Cao
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  4. Shan Liang
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Correspondence to Gao Tao.

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Tao, G., Liu, Z., Cao, J. et al. Local difference ternary sequences descriptor based on unsupervised min redundancy mutual information feature selection. Multidim Syst Sign Process 31, 771–791 (2020). https://doi.org/10.1007/s11045-018-0595-z

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