Image Retrieval by Regions: Coarse Segmentation and Fine Color Description
In Content-Based Image Retrieval systems, region-based queries allow more precise search than global ones. The user can retrieve similar regions of interest regardless their background in images. The definition of regions in thousands of generic images is a difficult key point, since it should not need user interaction for each image, and nevertheless be as close as possible to regions of interest (to the user). In this paper we first propose a new technique of unsupervised coarse detection of regions which improves their visual specificity. The Competitive Agglomeration (CA) classification algorithm, which has the advantage to automatically determine the optimal number of classes, is used.
The second key point is the region description which must be finer for regions than for images. We present a novel region descriptor of fine color variability: the Adaptive Distribution of Color Shades. It is based on color shades adaptively determined for each region at a high resolution: 5 million of potential different colors represented against few hundreds of predefined colors in existing descriptors.
Successful results of segmentation and region queries are presented on a database of 2500 generic images involving landscapes, people, objects, architecture, flora. . . .
KeywordsImage Retrieval Color Shade Region Query Color Description Region Description
Unable to display preview. Download preview PDF.
- 1.Del Bimbo and Vicario E., “Using weighted spatial relationships in retrieval by visual contents,” IEEE workshop on Image and Video Libraries, June 1998.Google Scholar
- 2.S.F. Chang J.R. Smith, “Visualseek: A fully automated content-based image query system,” in ACM Multimedia, 1996, pp. 87–98.Google Scholar
- 3.B. Moghaddam, H. Biermann, and D. Margaritis, “Defining image content with multiple regions of interest,” CBAIVL, 1999.Google Scholar
- 4.J. Malki, N. Boujemaa, C. Nastar, and A. Winter, “Region queries without segmentation for image retrieval by content,” in Visual Information and Information Systems, 1999, pp. 115–122.Google Scholar
- 5.Belongie S., Carson C., Greenspan H., and Malik J., “Color-and texture-based image segmentation using em and its application to content-based image retrieval,” Proc. Int. Conf. on Computer Vision (ICCV’98), 1998.Google Scholar
- 6.Deng Y. and Manjunath B., “An efficient low-dimensional color indexing scheme for region-based image retrieval,” ICASSP Proceedings, 1999.Google Scholar
- 7.Ma W. and B. Manjunath, “Edgeflow: A framework of boundary detection and image segmentation,” CVPR Proceedings, pp. 744–749, 1997.Google Scholar
- 9.Jia Li James Z. Wang and Gio Wiederhold, “Simplicity: Semantics-sensitive integrated matching for picture libraries,” PAMI, 2001.Google Scholar
- 10.C. Carson, M. Thomas, and S. Belongie, “Blobworld: A system for region-based image indexing and retrieval,” 1999.Google Scholar
- 12.Boujemaa N., “On competitive unsupervized clustering,” ICPR, 2000.Google Scholar
- 13.J. C. Bezdek, Pattern Recognition with Fuzzy Objective Functions, Plenum, New York NY, 1981.Google Scholar
- 14.J. Hafner H. Sawhney W. Aquitz M. Flickner and W. Niblack, “Efficient color histogram indexing for quadratic form distance functions,” PAMI, 1995.Google Scholar