Tag Clustering and Refinement on Semantic Unity Graph

Recently, there has been extensive research towards the user-provided tags on photo sharing websites which can greatly facilitate image retrieval and management. However, due to the arbitrariness of the tagging activities, these tags are often imprecise and incomplete. As a result, quite a few technologies has been proposed to improve the user experience on these photo sharing systems, including tag clustering and refinement, etc. In this work, we propose a novel framework to model the relationships among tags and images which can be applied to many tag based applications. Different from previous approaches which model images and tags as heterogeneous objects, images and their tags are uniformly viewed as compositions of Semantic Unities in our framework. Then Semantic Unity Graph (SUG) is introduced to represent the complex and high-order relationships among these Semantic Unities. Based on the representation of Semantic Unity Graph, the relevance of images and tags can be naturally measured in terms of the similarity of their Semantic Unities. Then Tag clustering and refinement can then be performed on SUG and the polysemy of images and tags is explicitly considered in this framework. The experiment results conducted on NUS-WIDE and MIR-Flickr datasets demonstrate the effectiveness and efficiency of the proposed approach.