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Object-based Image Retrieval Using Dominant Color Pair and Color Correlogram  

박기태 (Department of Computer Science and Engineering, Hanyang University)
문영식 (Department of Computer Science and Engineering, Hanyang University)
Publication Information
Abstract
This paper proposes an object-based image retrieval technique based on the dominant color pair information. Most of existing methods for content based retrieval extract the features from an image as a whole, instead of an object of interest. As a result, the retrieval performance tends to degrade due to the background colors. This paper proposes an object based retrieval scheme, in which an object of interest is used as a query and the similarity is measured on candidate regions of DB images where the object may exist. From the segmented image, the dominant color pair information between adjacent regions is used for selecting candidate regions. The similarity between the query image and DB image is measured by using the color correlogram technique. The dominant color pair information is robust against translation, rotation, and scaling. Experimental results show that the performance of the proposed method has been improved by reducing the errors caused by background colors.
Keywords
dominant color; image retrieval; color pair; correlogram; segmentation;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 J. Matas, R. Marik, J. Kittler, 'On representation and matching of multi-coloured objects', Proc. of IEEE International Conference on Computer Vision, pp. 726-732, June. 1995   DOI
2 J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih, 'Image Indexing Using Color Correlograms', Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762-768, 1997   DOI
3 M. J. Swain, 'Color Indexing', International Journal of Computer Vision. Vol. II -32, pp. 11-32, 1991   DOI
4 G. Pass and R. Zabih, 'Histogram Refinement for Content-based Image Retrieval', ACM, Journal of Multimedia System, Vol 7. No. 3 pp. 234-240, 1999   DOI
5 ISO/IEC JTC1/SC29/WG1 'Core Experiment on MPEG-7 Color and Texture Descriptors', Doc. N2819, MPEG Vancouber Meeting, July 1999
6 C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkobic and W. Equitz, 'Efficient and Effective Querying by Image Content', Journal of Intelligent Information Systems, Vol. 3, pp. 231-262, 1994   DOI
7 C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkobic and W. Equitz, 'Efficient and Effective Querying by Image Content', Journal of Intelligent Information Systems, Vol. 3, pp. 231-262, 1994   DOI
8 R. Brunelli and O. Mich, 'Image Retrieval by Examples', IEEE Trans. on Multimedia, Vol. 2, No. 3, pp. 164-171, Sep 2000   DOI   ScienceOn
9 A. Yoshitaka and T. Ichikawa, 'A Survey on Content-based Retrieval for Multimedia Data-bases', IEEE Trans. on Knowledge and Data Engineering, Vol. 11, No. 1, pp. 81-93, 1999   DOI   ScienceOn
10 J. K. Wu, 'Content-based Indexing of Multimedia Databases', IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No. 6, pp. 978-989, 1997   DOI   ScienceOn
11 M. Das, E. M. Riseman and B. Draper, 'Focus: Searching for Multi-colored Objects in a Diverse Image Database,' Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762-768, 1997   DOI
12 W. Y. Ma and B. S. Manjunath, 'Netra: A Toolbox for Navigating Large Image Database', IEEE International Conference on Image Processing, pp. 568-571, 1997   DOI
13 B. S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan, Akio Yamada 'Color and Texture Descriptors', IEEE Tans. Circuits and Systems for Video Technology. Vol. 11, No. 6, June 2001
14 D. Wang, 'Unsupervised Video Segmentation-based on Watersheds and Temporal Tracking', IEEE Trans. on Circuits and System for Video Technology, Vol. 8, No. 5, pp. 539-546, 1998   DOI   ScienceOn
15 J. R. Smith and S. F. Chang, 'VisualSEEk : A Fully Automated Content-based Image Query System', ACM Multimedia, Boston MA, 1996   DOI