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Summary: 34 9 Vol. 34, No. 9
2008 9 ACTA AUTOMATICA SINICA September, 2008
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TP391
Cluster-based Face Image Retrieval and Its Relevance Feedback
YANG Zhi-Guang1
AI Hai-Zhou1
Abstract This paper proposes a novel cluster-based face image retrieval algorithm. By using normalized cuts (NCuts) to
cluster faces in each time span into optimal partitions, various face images of the same character can be grouped together.
A face recognition classifier learned by real AdaBoost is used for measuring similarity between two faces, and a similarity
measure for the query face and the face cluster is further proposed for retrieval. To further improve the performance, we
design an online step, in which users can interactively label false positives and missing retrieval, so that some constraints
are involved to revise the clustering results. The algorithm is integrated into an automatic retrieval system, and the
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