Iteratively clustering web images based on link and attribute reinforcements

Image clustering is an important research topic which contributes to a wide range of applications. Traditional image clustering approaches are based on image content features only, while content features alone can hardly describe the semantics of the images. In the context of Web, images are no longer assumed homogeneous and "flatdistributed but are richly structured. There are two kinds of reinforcements embedded in such data: 1) the reinforcement between attributes of different data types (intra-type links reinforcements); and 2) the reinforcement between object attributes and the inter-type links (inter-type links reinforcements). Unfortunately, most of the previous works addressing relational data failed to fully explore the reinforcements. In this paper, we propose a reinforcement clustering framework to tackle this problem. It reinforces images and texts' attributes via inter-type links and inversely uses these attributes to update these links. The iterative reinforcing nature of this framework promises the discovery of the semantic structure of images, which is the basis of image clustering. Experimental results show the effectiveness of our proposed framework.