Content-Irrelevant Tag Cleansing via Bi-Layer Clustering and Peer Cooperation

Abstract

User-provided tags for social images have facilitated many fields, such as social image organization, summarization and retrieval. Since the users utilize their own knowledge and personalized language to describe the visual content of social images, these social tags are too imprecise and ambiguous to exploit the social image tagging. In this paper, we discover the content-similar images (peers) and leverage the relationships among these images (peer cooperation) to handle the problem of content-irrelevant tags. A bi-layer clustering framework for discovering content-similar images is proposed to divide image collection into different groups, and the tags of peers in these groups are cleaned jointly based on tag statistics and relevance. The relevance of tags measured by Google Distance is used to generate the first-layer clustering and then the bi-modality similarity of images is used to perform the second-layer clustering. Based on the bi-layer clustering, we utilize peers in a group to identify their content-irrelevant tags. Finally, an extended Fisher's criterion is proposed to decide the proper number of content-irrelevant tags. To verify the effectiveness of our proposed technique, we conduct the experiments on the social images of Flickr and the standard benchmark. The comparison experiments show that our proposed algorithm achieves positive results for tag cleansing and image retrieval.