Learning semantic concepts from user feedback log for image retrieval
To improve the performance of image retrieval systems, the well-known semantic gap needs to be bridged. Relevance feedback provides a strategy for learning semantic concepts from visual features. This paper reports a novel framework to learn semantic concepts from the accumulated user feedback log. The semantic concepts consist of two categories: explicit semantics and implicit semantics. The former can be directly estimated by analyzing the user-provided feedback log. The latter is learned according to the obtained explicit semantics. Finally, both explicit and implicit semantics are applied to an image retrieval system. Experiments on 10,000 images show the superiority of the proposed method