Abstract
Surfimage is a versatile content-based image retrieval system allowing both efficiency and flexibility, depending on the application. Surfimage uses the query-by-example approach for retrieving images and integrates advanced features such as image signature combination, multiple queries, query refinement, and partial queries. The classic and advanced features of Surfimage are detailed hereafter. Surfimage has been extensively tested on dozens of databases, demonstrating performance and robustness. Several experimental results are presented in the paper.
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References
S. Belongie, C. Carson, H. Greenspan, and J. Malik. Color-and texture-based image segmentation using em and its application to content-based image retrieval. In Proceedings of the Sixth International Conference on Computer Vision (ICCV’ 98), Bombay, January 1998.
I. Cox et al. PicHunter: Bayesian relevance feedback for image retrieval. In Proceedings of 13th International Conference on Pattern Recognition, Vienna, Austria, 1996.
M. Flickner et al. Query by image and video content: the qbic system. IEEE Computer, 28(9), 1995.
A. Jain and A. Vailaya. Image retrieval using color and shape. Pattern Recognition, 29(8), 1996.
T. Minka and R. Picard. Interactive learning using a society of models. Pattern Recognition, 30(4), 1997.
H. Murase and S. K. Nayar. Visual learning and recognition of 3D objects from appearance. International Journal of Computer Vision, 14(5), 1995.
C. Nastar and M. Mitschke. Real-time face recognition using feature combination. In 3rd IEEE International Conference on Automatic Face-and Gesture-Recognition (FG’98), Nara, Japan, April 1998.
C. Nastar, M. Mitschke, and C. Meilhac. Efficient query refinement for image retrieval. In Computer Vision and Pattern Recognition (CVPR’ 98), Santa Barbara, June 1998.
C. Nastar, M. Mitschke, C. Meilhac, and N. Boujemaa. Surfimage: a flexible content-based image retrieval system. In ACM-Multimedia 1998, Bristol, England, September 1998.
C. Nastar, B. Moghaddam, and A. Pentland. Flexible images: Matching and recognition using learned deformations. Computer Vision and Image Understanding, 35(2), February 1997.
M. Ortega, Y. Rui, K. Chakrabarti, S. Mehrotra, and T. Huang. Supporting similarity queries in MARS. In ACM Multimedia, Seattle, November 1997.
A. Pentland, R. Picard, and S. Sclaroff. Photobook: Tools for content-based manipulation of image databases. Int. Journal of Comp. Vision, 18(3), 1996.
R. Picard, T. Minka, and M. Szummer. Modeling subjectivity in image libraries. In IEEE Int. Conf. on Image Proc., Lausanne, September 1996.
Y. Rui, T. Huang, S. Mehrotra, and M. Ortega. A relevance feedback architecture for content-based multimedia information systems. In Workshop on Content Based Access of Image and Video Libraries, Porto Rico, June 1997.
M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 1991.
A. Vellaikal and C. Kuo. Joint spatial-spectral indexing of jpeg compressed data for image retrieval. In Int’l Conf. on Image Proc., Lausanne, 1996.
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© 1998 Springer-Verlag Berlin Heidelberg
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Nastar, C., Mitschke, M., Boujemaa, N., Meilhac, C., Bernard, H., Mautref, M. (1998). Retrieving Images by Content: The Surfimage System. In: Advances in Multimedia Information Systems. MIS 1998. Lecture Notes in Computer Science, vol 1508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49651-3_11
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DOI: https://doi.org/10.1007/3-540-49651-3_11
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