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Categorical image retrieval through genetically optimized support vector machines (GOSVM) and hybrid texture features

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Abstract

Content-based image retrieval (CBIR) systems provide a potential solutions of retrieving semantically similar images from large image repositories against any query image. The research community is competing for more efficient and effective methods of content-based image retrieval, so they can be employed in serving time critical applications in scientific and industrial domains. In this paper, we have combined genetic algorithm and support vector machines to reduce the existing gap between high-level semantic content of the images and the information provided by their low-level descriptors. To maximize the performance of proposed technique, an efficient feature extraction method is introduced, which is based on the concept of in-depth texture analysis. To further enhance the capabilities of proposed method, we employed a way through which the risk of mis-associations can be avoided. To justify the effectiveness of the proposed method, we compared it against several popular CBIR techniques and show a significant improvement in terms of accuracy and stability based on Corel image gallery.

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Author information

Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Aun Irtaza & M. Arfan Jaffar

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

    Aun Irtaza

  3. College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    M. Arfan Jaffar

Authors
  1. Aun Irtaza
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  2. M. Arfan Jaffar
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Correspondence to M. Arfan Jaffar.

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Irtaza, A., Jaffar, M.A. Categorical image retrieval through genetically optimized support vector machines (GOSVM) and hybrid texture features. SIViP 9, 1503–1519 (2015). https://doi.org/10.1007/s11760-013-0601-8

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  • DOI: https://doi.org/10.1007/s11760-013-0601-8

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