Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Modeling social strength in social media community via kernel-based learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
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Tag-based social image search predominately focus on using user-annotated tags to find out the results of user query. However, the performance of tag-based social image search is usually unable to satisfy the needs of users. In this paper, we propose a novel framework based on Social Relationship Graph for Social Image Search (SRGSIS), which involves two stages. In the first stage, we use heterogeneous data from multiple modalities to build a social relationship graph. Then, for the given query keywords, we execute an efficient keyword search algorithm over the social relationship graph and obtain top-k candidate results based on relevance score. We model these results as the answer trees connecting keyword nodes that match keywords in the query. In the second stage, for refining the candidate results, each image in social relationship graph is represented as a region adjacency graph by using the visual content of image. We further model these region adjacency graphs as a closure tree and compute approximate graph similarity between the candidate results and the closure tree to obtain more desirable results. Extensive experimental results demonstrate the effectiveness of the proposed approach.