Optimizing learning in image retrieval
Rui, Y.; Huang, T.
Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on
Volume 1, Issue , 2000 Page(s):236 - 243 vol.1
Digital Object Identifier 10.1109/CVPR.2000.855825
Summary:Combining learning with vision techniques in interactive image
retrieval has been an active research topic during the past few years.
However, existing learning techniques either are based on heuristics or
fail to analyze the working conditions. Furthermore, there is almost no
in depth study on how to effectively learn from the users when there are
multiple visual features in the retrieval system. To address these
limitations, in this paper we present a vigorous optimization
formulation of the learning process and solve the problem in a
principled way. By using Lagrange multipliers, we have derived explicit
solutions, which are both optimal and fast to compute. Extensive
comparisons against state-of-the-art techniques have been performed.
Experiments were carried out on a large-size heterogeneous image
collection consisting of 17,000 images. Retrieval performance was tested
under a wide range of conditions. Various evaluation criteria, including
precision-recall curve and rank measure, have demonstrated the
effectiveness and robustness of the proposed technique
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