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Tetragonal Local Octa-Pattern (T-LOP) based image retrieval using genetically optimized support vector machines

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Abstract

The enormous increase in digital image collections motivates the research community to propose powerful Content Based Image Retrieval (CBIR) algorithms to employ in critical scientific domains. In this paper, we have proposed Tetragonal Local Octa-Patterns for CBIR that are based on the direction of center pixel and generate an 8-bit octa-pattern. Neighbors at three diagonal locations are then used to generate Tetragonal Octa-Patterns. In order to enhance the precision, Genetic algorithm has been applied on obtained features to resolve the class imbalance problem for better classification through SVM. Experimental results prove the reliability of method by comparing against state-of-the-art methods in terms of precision and recall.

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Authors and Affiliations

  1. Department of Electrical, Electronics and Communication Engineering, University of Engineering and Technology Lahore (Faisalabad Campus), Faisalabad, Pakistan

    Zain Shabbir

  2. Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

    Aun Irtaza

  3. Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan

    Ali Javed

  4. School of Computer Science and Engineering, Korea University of Technology and Education, Cheonan, South Korea

    Muhammad Tariq Mahmood

Authors
  1. Zain Shabbir
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  2. Aun Irtaza
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  3. Ali Javed
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  4. Muhammad Tariq Mahmood
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Correspondence to Zain Shabbir.

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Shabbir, Z., Irtaza, A., Javed, A. et al. Tetragonal Local Octa-Pattern (T-LOP) based image retrieval using genetically optimized support vector machines. Multimed Tools Appl 78, 23617–23638 (2019). https://doi.org/10.1007/s11042-019-7597-1

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  • DOI: https://doi.org/10.1007/s11042-019-7597-1

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