Partial relevance in interactive facial image retrieval

Authors:
Zhirong Yang;Jorma Laaksonen
Affiliations:
Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo, Finland;Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo, Finland
Venue:
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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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.