Partial relevance in interactive facial image retrieval
Zhirong Yang and Jorma Laaksonen
In: 3rd International Conference on Advances in Pattern Recognition, 22-25 Aug 2005, Bath, United Kingdom.

Abstract

For databases of facial images, where each subject has only a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A novel method is proposed to relieve this problem by applying partial relevance to the interactive retrieval. This work extends an existing content-based image retrieval system, PicSOM, by relaxing the relevance criterion in the early rounds of the retrieval. Moreover, we apply linear discriminant analysis as a preprocessing step before training the Self-Organizing Maps (SOMs) so that the resulting SOMs have stronger discriminative power. The results of simulated retrieval experiments suggest that for semantic classes such as ``black persons'' or "bearded persons" the first image which depicts the target subject can be obtained three to six times faster than by retrieval without the partial relevance.

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