Multimedia Tools and Applications

, Volume 72, Issue 2, pp 1961–1984 | Cite as

Image retrieval based on quadtree classified vector quantization

  • Hsin-Hui Chen
  • Jian-Jiun Ding
  • Hsin-Teng Sheu


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.


Quadtree segmentation Vector quantization Classified vector quantization Image indexing Content-based image retrieval 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Graduate Institute of Communication EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Electrical EngineeringTung-Nan UniversityXinbeiTaiwan

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