Semi-supervised hierarchical clustering for personalized web image organization - MENG, Lei (Institutional Knowledge at Singapore Management University, 2012)
Existing efforts on web image organization usually transform the task into surrounding text clustering. However, Current text clustering algorithms do not address the problem of insufficient statistical information for image representation and noisy tags which greatly decreases the clustering performance while increases the computational cost. In this paper, we propose a two-step semi-supervised hierarchical clustering algorithm, Personalized Hierarchical Theme-based Clustering (PHTC), for web image organization. In the first step, the Probabilistic Fusion ART (PF-ART) is proposed for grouping semantically similar images and simultaneously learning the probabilistic distribution of tag occurrence for mining the key tags/topics of clusters. In this way, the side-effect of noisy tags can be largely eliminated. Moreover, PF-ART can incorporate user preference for semi-supervised learning and provide users a direct control of clustering results. In the second step, a novel agglomerative merging strategy based on Cluster Semantic Relevance, proposed for measuring the semantic similarity between clusters, is employed for associating the clusters by generating a semantic hierarchy. Different from existing hierarchical clustering algorithms, the proposed merging strategy can provide a multi-branch tree structure which is more systematic and clearer than traditional binary tree structure. Extensive experiments on two real world web image data sets, namely NUS-WIDE and Flickr, demonstrate the effectiveness of our algorithm for large web image data sets.