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Texture image retrieval using hybrid directional Extrema pattern

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Abstract

In this paper, a new descriptor Hybrid Directional Extrema Pattern (HDEP) for the retrieval of texture images by integrating the concept of Weight Difference Directional Local Extrema Pattern (WD_DLEP) and Directional Local Extrema Pattern (DLEP) is proposed. The texture patterns are computed for four principle directions i.e., 0°, 45°, 90°, 135°. The proposed approach considers the difference between central pixel and corresponding neighboring pixels in the specified directions. This difference is used as weight in next stage. This weight is compared with a user-defined threshold to determine the value of strong bits in a feature vector. Experimental evaluation on three benchmark datasets (Brodatz, VisTex and Describable Textures Dataset) illustrates the better performance of proposed system with the other state-of-the-art techniques on two basic parameters i.e., average retrieval rate and time. The proposed approach is evaluated against various state-of-the-art texture image-retrieval systems based on local binary pattern, directional local extrema pattern, local tetra pattern, block-based local binary pattern, center-symmetric local binary pattern and wavelet. Significant improvement has been achieved in image retrieval performance due to assignment of weight in the pattern generation process. Further, the proposed approach is capable of differentiating different texture patterns more efficiently because it uses magnitude of pixel differences to determine the value of current pixel rather than the sign of pixel differences.

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Authors and Affiliations

  1. Manipal University Jaipur, Jaipur, Rajasthan, India

    Ghanshyam Raghuwanshi

  2. Jaypee University of Engineering and Technology, Raghogarh, Guna, MP, 473226, India

    Vipin Tyagi

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  1. Ghanshyam Raghuwanshi
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  2. Vipin Tyagi
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Correspondence to Vipin Tyagi.

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Raghuwanshi, G., Tyagi, V. Texture image retrieval using hybrid directional Extrema pattern. Multimed Tools Appl 80, 2295–2317 (2021). https://doi.org/10.1007/s11042-020-09618-7

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  • DOI: https://doi.org/10.1007/s11042-020-09618-7

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