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A new analysis framework for relevance feedback-driven similarity measure refinement in content-based image retrieval

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2 Author(s)
Jones, B.C. ; Vanderbilt Univ., Nashville, TN, USA ; Wilkes, D.M.

Many recent content-based image retrieval techniques utilize relevance feedback (RF) from the user to adjust the system response to better meet user expectations. One school of RF-based methods uses a weighted Minkowski distance metric to assess similarity, and adjusts the weights to refine query response. A new method of estimating these weight vectors is presented which outperforms existing methods, particularly for the important case of limited training data. A new objective function is presented for an iterative optimization routine which more closely aligns optimization goals with true system goals. A new analysis framework is presented in the derivation of this technique which is useful for understanding the limitations of many RF methods.

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Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on  (Volume:1 )

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