Content-based image retrieval using joint correlograms
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The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.
KeywordsContent-based image retrieval Color histograms Correlograms
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- 3.Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P (1995) Query by image and video content: the QBIC system. IEEE Computer Society Press, Los Alamitos, CAGoogle Scholar
- 5.Gupta A, Jain R (1997) Visual information retrieval. Commun ACM 40:71–79Google Scholar
- 6.Huang J, Kumar SR, Mitra M, Zhu W-J, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of the 1997 conference on computer vision and pattern recognition, pp 762–768. IEEE Computer Society, Washington, DCGoogle Scholar
- 8.Pass G, Zabih R (1996) Histogram refinement for content-based image retrieval. In: Proceedings of the 3rd IEEE workshop on applications of computer vision, pp 96–102. IEEE Computer Society, Washington, DCGoogle Scholar
- 10.Pass G, Zabih R, Miller J (1996) Comparing images using color coherence vectors. In: Proc. ACM Intern. Conf. Multimedia, pp 65–73. ACM Press, New York, NYGoogle Scholar
- 11.Scalaroff S, Taycher L, La Cascia M (1997) Imagerover: a content-based image browser for the world wide web. In: IEEE workshop on content-based access and video libraries. IEEE Computer Society, Washington, DCGoogle Scholar
- 12.Stricker M, Swain M (1994) The capacity of color histogram indexing. IEEE Press, Piscataway, NJ (pp 704–708)Google Scholar