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Image retrieval based on quadtree classified vector quantization

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Abstract

In this paper, a color image retrieval scheme based on quadtree classified vector quantization (QCVQ) is proposed. This scheme not only captures intra-block correlation but also exploits the visual importance of image blocks to efficiently describe the content of images in a compressed domain. In the proposed algorithm, a query image is first divided by quadtree segmentation and then classified into smooth and high-detail blocks. For high-detail blocks, the local thresholding classifier with 28 edge binary templates is employed to extract a variety of visually important regions which are edge intensive. After all of the blocks in the image are encoded by the pre-trained QCVQ codebook, the indices in the compressed domain are obtained. Finally, the frequencies of indices are counted to build the index histogram as a feature of the query image. Simulation results demonstrate that our proposed scheme yields the better retrieval performance compared to the well-known vector quantization (VQ)-based image retrieval method and three other techniques. These results show that quadtree segmentation and edge style classification are indeed helpful for improving the performance of content-based image retrieval.

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Authors and Affiliations

  1. Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan, 10617

    Hsin-Hui Chen & Jian-Jiun Ding

  2. Department of Electrical Engineering, Tung-Nan University, Xinbei, Taiwan, 22202

    Hsin-Teng Sheu

Authors
  1. Hsin-Hui Chen
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  2. Jian-Jiun Ding
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  3. Hsin-Teng Sheu
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Correspondence to Jian-Jiun Ding.

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Chen, HH., Ding, JJ. & Sheu, HT. Image retrieval based on quadtree classified vector quantization. Multimed Tools Appl 72, 1961–1984 (2014). https://doi.org/10.1007/s11042-013-1492-y

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  • DOI: https://doi.org/10.1007/s11042-013-1492-y

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