Abstract
In this paper a new image representation for compressed domain image retrieval and an image retrieval system are presented. To represent images compactly and hierarchically, multiple features such as color and texture features directly extracted from DCT coefficients are structurally organized using vector quantization. To train the codebook, a new Minimum Description Length vector quantization algorithm is used and it automatically decides the number of code words. To compare two images using the proposed representation, a new efficient similarity measure is designed. The new method is applied to an image database with 1,005 pictures. The results demonstrate that the method is better than two typical histogram methods and two DCT-based image retrieval methods.
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FAN Yun was born in 1976. He received his Ph.D. degree from the School of Electronics Engineering, National University of Defense Technology in 2002. His research interests include content-based retrieval, image understanding, and pattern recognition.
WANG Runsheng was born in 1941. He is now a professor and a Ph.D. supervisor in the School of Electronics Engineering, National University of Defense Technology. His research interests include image understanding, pattern recognition, and information fusion.
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Fan, Y., Wang, R. An image retrieval method using DCT features. J. Compt. Sci. & Technol. 17, 865–873 (2002). https://doi.org/10.1007/BF02960778
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DOI: https://doi.org/10.1007/BF02960778