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The practical applications of HLBP texture descriptor

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Abstract

The traditional Local binary pattern (LBP) compares the central pixel with all 8-neighboring pixels in a 3 × 3 pixel window to generate LBP codes. However, its circular structure may result in similar LBP codes for different structural patterns. The technique also resulted in high-dimensional feature vectors, which cause a computational burden. Researchers have proposed several LBP variants; however, none of them addressed the aforementioned issues. This paper proposes a Hyperbolic local binary pattern (HLBP) that follows the hyperbolic structure to extract the discriminative features. In particular, HLBP combines 3 × 5 Horizontal hyperbolic-LBP (HHLBP) and 5 × 3 Vertical hyperbolic-LBP (VHLBP) movements. Experiments are conducted on facial and texture image databases to test the robustness of HLBP. The experiment outcomes demonstrate that HLBP with comparative low-dimensional feature vector outperforms the state-of-the-art descriptors.

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Data Availability

Five publicly databases are analysed during the current study. These databases include AT&T [1], GT [18], JAFFE [45], IFDB [15], and Brodatz [12]. The link to these databases are available in the reference section.

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Acknowledgements

The pattern research center in Iran is to be thanked by the authors for providing the IFDB face image database. The editors and anonymous reviewers provided insightful comments and helpful ideas that have been included into this manuscript with thanks from the authors.

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  1. Indian Institute of Technology, Roorkee, India

    Nitin Arora & Subhash C. Sharma

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  1. Nitin Arora
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Arora, N., Sharma, S.C. The practical applications of HLBP texture descriptor. Multimed Tools Appl 82, 29379–29404 (2023). https://doi.org/10.1007/s11042-023-14406-0

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