Abstract
In this paper, we investigate the combination of image semantic classification with content-based image retrieval. A flexible scheme is proposed to take advantage of image classification, which may be obtained manually or automatically, to enhance image retrieval. In this scheme, a semantic feature vector is composed for an image based on its class membership information, and is combined with low-level features in image retrieval. Relevance feedback techniques are also used to adjust both the semantic feature and low-level features of the query in order to better reflect the user’s intention. Experimental results on a collection of 10,000 images with manual classification demonstrate the effectiveness of the proposed method.
This work was performed at Microsoft Research Asia.
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Bradshaw B. (2000) Semantic Based Image Retrieval: A Probabilistic Approach. Proc. ACM Multimedia, Los Angeles, California.
Baeza-Yates R. and Ribeiro B., editors. (1999) Modern Information Retrieval. Addison Wesley.
La Cascia M., Sethi S., and Sclaroff S. (1998) Combining textual and visual cues for content-based image retrieval on the World Wide Web. In Proc. IEEE Workshop on Content-based Access of Image and Video Libraries, Santa Barbara, CA.
Cox I. J., Miller M. L., Omohundro S., and Yianilos P. (1996) PicHunter: Bayesian Relevance Feedback for Image Retrieval. In Int. Conf. on Pattern Recognition, Vienna, Austria.
Cox I. J., Ghosn J., Miller M. L., Papathomas T. V., and Yianilos P. N. (1997) Hidden annotation in content based image retrieval. In Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries, pages 76–81.
Flickner M., Sawhney H., Niblack W., Ashley J., Huang Q., and Dom B. et al, (1995) Query by Image and Video Content: The QBIC System. IEEE Computer, 28 (9).
Liu W., Sun Y., Zhang H.J. (2000) MiAlbum—A System for Home Photo Management Using the Semi-Automatic Image Annotation Approach. Proc. ACM MULTIMEDIA 2000, Los Angeles, California.
Lu Y., Hu Ch., Zhu X., Zhang H.J., Yang Q. (2000) A Unified Semantics and Feature Based Image Retrieval Technique Using Relevance Feedback. Proc. ACM MULTIMEDIA 2000, Los Angeles, California.
Pentland A., Picard R., and Sclaroff S. (1994) Photobook: Tools for Content-based Manipulation of Image Databases. In Proc. of the SPIE Conference on Storage and Retrieval of Image and Video Databases II, pages 34–47.
Rui Y., Huang T. S., Ortega M., Mehrotra S. (1998) Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. on Circuits and Systems for Video Technology, 8 (5).
Rui Y. and Huang T. S. (2000) Optimizing Learning In Image Retrieval. Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC.
Smith J. R. and Chang S. –F. (1996) Visualseek: a fully automated content-based image
query system. In Proc. of ACM Multimedia 96, pages 87–98, Boston MA USA.
Smith J. R., Chang S. –F. (1997) Visually Searching the Web for Content IEEE Multimedia
Magazine, Vol. 4 No. 3, pp. 12–20.
Squire D. M. and Pun T. (1998) Assessing agreement between human and machine clusterings of image databases. Pattern Recognition, Vol. 31, No. 12.
Szummer M. and Picard R. W. (1998) Indoor-Outdoor Image Classification. IEEE Int. Workshop on Content-based Access of Image and Video Databases.
Wang J. Z. (2000) SIMPLIcity: A region-based image retrieval system for picture libraries
and biomedical image databases. Proc. ACM Multimedia, Los Angeles, California.
Vailaya A., Jain A., and Zhang H.J. (1998) On Image Classification: City Image vs. Landscapes. Pattern Recognition, Vol. 31, No. 12, pp. 1921–1936.
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Wu, H., Li, M., Zhang, HJ., Ma, WY. (2002). Improving Image Retrieval with Semantic Classification Using Relevance Feedback. In: Zhou, X., Pu, P. (eds) Visual and Multimedia Information Management. VDB 2002. IFIP — The International Federation for Information Processing, vol 88. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35592-4_23
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DOI: https://doi.org/10.1007/978-0-387-35592-4_23
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