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Directional local co-occurrence patterns based on Haar-like filters

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Abstract

Content based image retrieval (CBIR) systems enable a quick retrieval of similar images from a large digital repository. However, the performance of the system is heavily reliant on the feature definition of images. The challenge lies in extracting suitable features that can work across a variety of datasets. In this paper, Haar-like local ternary co-occurrence pattern (HLTCoP) is designed as a feature for image retrieval applications. In HLTCoP, four different Haar-like filters are deployed to capture directional information of the image and two different local neighborhoods are considered to obtain the local patterns around every pixel. Thereafter, co-occurrence between two filtered images is computed to construct the HLTCoP feature. Additionally, color information is extracted using histograms of hue and saturation planes. Image retrieval performance is verified on diversified benchmark datasets, Corel 10k, CMU-PIE and MIT VisTex. Significant improvement is achieved in comparison to the existing techniques.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, 201304, India

    Megha Agarwal

  2. Department of Electronics and Communication Engineering, Netaji Subhas University of Technology, Delhi, 110078, India

    Amit Singhal

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  1. Megha Agarwal
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  2. Amit Singhal
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Correspondence to Amit Singhal.

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Agarwal, M., Singhal, A. Directional local co-occurrence patterns based on Haar-like filters. Multimed Tools Appl 81, 1109–1123 (2022). https://doi.org/10.1007/s11042-021-11361-6

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  • DOI: https://doi.org/10.1007/s11042-021-11361-6

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