Abstract
To date most “content-based image retrieval” (CBIR) techniques rely on global attributes such as color or texture histograms which tend to ignore the spatial composition of the image. In this paper, we present an alternative image retrieval system based on the principle that it is the user who is most qualified to specify the query “content” and not the computer. With our system, the user can select multiple “regions-of-interest” and can specify the relevance of their spatial layout in the retrieval process. We also derive similarity bounds on histogram distances for pruning the database search. This experimental system was found to be superior to global indexing techniques as measured by statistical sampling of multiple users' “satisfaction” ratings.
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References
C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Region-based image querying,” in Proc. IEEEWorkshop on Content-Based Access of Image and Video Libraries, June 1997.
S. Chang, Q. Shi, and S. Yan., “Iconic indexing using 2-D strings,” IEEE Trans. on Pattern Analysis & Machine Intelligence, Vol. 9, No. 3, pp. 413–428, 1987.
K. Fukunaga, Introduction to Statistical Pattern Recognition. Academic Press: 1971.
A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin, Bayesian Data Analysis. Chapman & Hall: 1995.
N. Howe, “Percentile blobs for image similarity,” in Proc. IEEEWorkshop on Content-Based Access of Image and Video Libraries, June 1998.
J. Huang, S.K. Kumar, M. Mitra, W. Zhu, and R. Zabih, “Image indexing using color correlograms,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1997.
P. Lipson, E. Grimson, and P. Sinha, “Configuration based scene classification and image indexing,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1997.
J. Malki, N. Boujemaa, C. Nastar, and A. Winter, “Region queries without segmentation for image retrieval by content,” in Third International Conference on Visual Information Systems (Visual'99), Amsterdam, June 1999.
MIT Media Laboratory. Vistex vision texture database, 1995.
A. Papoulis, Probability, Random Variables, and Stochastic Processes. McGraw Hill: 1991.
B. Schiele and J.L. Crowley, “Object recognition using multidimensional receptive field histograms,” in European Conference on Computer Vision, Volume 1, ECCV, April 1996, pp. 619–619.
S. Sclaroff, L. Taycher, and M. La Cascia, “Imagerover: A content-based image browser for the world wide web,” in Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries, June 1997.
J. R. Smith, Integrated Spatial and Feature Image Systems: Retrieval, Compression and Analysis. PhD thesis, Columbia University, 1997.
M. Swain and D. Ballard, “Color indexing,” International Journal of ComputerVision,Vol. 7, No. 1, pp. 11–32, 1991.
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Moghaddam, B., Biermann, H. & Margaritis, D. Regions-of-Interest and Spatial Layout for Content-Based Image Retrieval. Multimedia Tools and Applications 14, 201–210 (2001). https://doi.org/10.1023/A:1011355417880
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DOI: https://doi.org/10.1023/A:1011355417880