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Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval

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Abstract

A new image retrieval technique using local neighborhood difference pattern (LNDP) has been proposed for local features. The conventional local binary pattern (LBP) transforms every pixel of image into a binary pattern based on their relationship with neighboring pixels. The proposed feature descriptor differs from local binary pattern as it transforms the mutual relationship of all neighboring pixels in a binary pattern. Both LBP and LNDP are complementary to each other as they extract different information using local pixel intensity. In the proposed work, both LBP and LNDP features are combined to extract the most of the information that can be captured using local intensity differences. To prove the excellence of the proposed method, experiments have been conducted on four different databases of texture images and natural images. The performance has been observed using well-known evaluation measures, precision and recall and compared with some state-of-art local patterns. Comparison shows a significant improvement in the proposed method over existing methods.

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Acknowledgments

This work was supported by the Ministry of Human Resource and Development (MHRD) grant, India under grant MHRD-02-23-200-304. The authors would like to thank the editor and anonymous reviewers for thoughtful comments and valuable suggestions to improve the quality, which have been incorporated in this paper.

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  1. Electrical Engineering Department, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujrat, 382355, India

    Manisha Verma

  2. Computer Science & Engineering Department, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, India

    Balasubramanian Raman

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  1. Manisha Verma
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  2. Balasubramanian Raman
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Correspondence to Manisha Verma.

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Verma, M., Raman, B. Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval. Multimed Tools Appl 77, 11843–11866 (2018). https://doi.org/10.1007/s11042-017-4834-3

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