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Multi-scale region perpendicular local binary pattern: an effective feature for interest region description

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Abstract

This paper proposes the perpendicular local binary pattern (PLBP) for efficiently describing textures in an interest region. Its novelty is two-fold: (1) the candidate generation scheme provides a set of patterns for each pixel, instead of conventionally assigning one pattern per pixel, and (2) an adaptive threshold based on the image contrast of a region is used. These modifications successfully enhance the robustness of PLBP to Gaussian noise as well as in near-uniform regions. We introduce the novel multi-scale region PLBP descriptor, which adopts the PLBP as its core feature. It defines multiple support regions from an interest point, sequentially performs ring-shaped and intensity order-based segmentations on each region, and pools PLBPs to corresponding segments. These steps are controlled easily by a set of parameters, thus offering high flexibility. Experimental results on challenging benchmarks, including three datasets of image matching and two datasets of object recognition, demonstrate the effectiveness of the proposed descriptor in handling common photometric and geometric transformations. It significantly improves the robustness, compared with current state-of-the-art descriptors, while maintaining a reasonable operational cost.

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Authors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, 923-1292 , Ishikawa, Japan

    Thao-Ngoc Nguyen & Kazunori Miyata

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  1. Thao-Ngoc Nguyen
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  2. Kazunori Miyata
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Correspondence to Thao-Ngoc Nguyen.

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Nguyen, TN., Miyata, K. Multi-scale region perpendicular local binary pattern: an effective feature for interest region description. Vis Comput 31, 391–406 (2015). https://doi.org/10.1007/s00371-014-0934-5

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