Learning and inferring a semantic space from user's relevance feedback for image retrieval

ABSTRACT

As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so the system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.

REFERENCES

• 1: Dagan, I., Karov, Y., and Roth, D. "Mistaken-driven learning in text categorization," Proc of the second conf. on empirical methods in natural language processing, pp. 55--63, 1997.
• 2: Nick Littlestone, Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm, Machine Learning, v.2 n.4, p.285-318, April 1988 [doi>10.1023/A:1022869011914]
• 3: Gerard Salton , Michael J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, 1986
• 4: He, Xiaofei, Ma, W.-Y., King, O., Li, M., Zhang, H.J., "Learning and inferring a semantic space from user's relevance feedback for image retrieval," Microsoft Technical Report, MSR-TR-2002-62, Microsoft Research. Apr. 2002.
• 5: Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
• 6: Ma, W.-Y. and Zhang, H.J., Content-based image indexing and retrieval, Handbook of Multimedia Computing, Chapter 11, CRC Press, 1999.

INDEX TERMS

Keywords:
image retrieval, learning, user's relevance feedback