NDLI: Image retrieval based on quadtree classified vector quantization

Content Provider	Springer Nature Link
Author	Chen, Hsin Hui Ding, Jian Jiun Sheu, Hsin Teng
Copyright Year	2013
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.
Starting Page	1961
Ending Page	1984
Page Count	24
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	72
Issue Number	2
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2013-05-12
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Quadtree segmentation Vector quantization Classified vector quantization Image indexing Content-based image retrieval Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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