Boosting Face Retrieval by using Relevant Set Correlation Clustering

Duy-Dinh Le, Shin'ichi Satoh, Michael E. Houle

Abstract: We present a method to improve the performance of face retrieval in news videos by using the relevant-set correlation (RSC) clustering model. In this method, faces of a person are firstly retrieved by finding in shots whose associated transcripts contain that person's name. Then, by using the RSC clustering, these faces are organized into clusters and only representative faces of these clusters are introduced to users. As a result, the retrieval performance is significantly increased since only a small number of faces belonging to the clusters relevant to the target person are returned instead of all initial retrieved faces. The contribution of new RSC clustering model for this problem is two-fold: First, it can automatically determine an appropriate number of clusters and discards a large number of irrelevant faces. Second, since the precision of clusters can be controlled easily by a threshold, high precision clusters can be obtained and treated as labeled sets so that they can be used for feature selection by using the linear discriminant analysis (LDA) method. Experiments on the TRECVID 2004 dataset showed that the retrieval performance of the proposed method outperforms other existing methods.