Tags associated with images significantly promote the development of social image retrieval. However, these user-annotated tags suffer the problems of noise and inconsistency, which limits the role they play in image retrieval. In this paper, we build a novel model to learn the tag relevance based on the context analysis for each tag. In our model, we firstly consider the user tagging habits and use a multi-model association network to capture the tag-tag relationship and tag-image relationship, and then accomplish the random-walk over the tag graph for each image to refine the tag relevance. Different from the earlier research work related to tag ranking, our contributions focuse on the globally-comparable tag relevance measure (i.e., can be compared across different images) and better tag relevance learning model by detailed context analysis for each tag. Our experiments on the public data from Flickr have obtained very positive results.
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