Optimizing Learning in Image Retrieval

11 years 3 months ago
Optimizing Learning in Image Retrieval
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-ofthe-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...
Yong Rui, Thomas S. Huang
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2000
Where CVPR
Authors Yong Rui, Thomas S. Huang
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