Visual Reranking through Weakly Supervised Multi-Graph Learning

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Conference Proceeding
Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013, 2013, pp. 2600 - 2607
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Visual reranking has been widely deployed to refine the quality of conventional content-based image retrieval en- gines. The current trend lies in employing a crowd of re- trieved results stemming from multiple feature modalities to boost the overall performance of visual reranking. Howev- er, a major challenge pertaining to current reranking meth- ods is how to take full advantage of the complementary property of distinct feature modalities. Given a query im- age and one feature modality, a regular visual reranking framework treats the top-ranked images as pseudo positive instances which are inevitably noisy, difficult to reveal this complementary property, and thus lead to inferior ranking performance. This paper proposes a novel image rerank- ing approach by introducing a Co-Regularized Multi-Graph Learning (Co-RMGL) framework, in which the intra-graph and inter-graph constraints are simultaneously imposed to encode affinities in a single graph and consistency across d- ifferent graphs. Moreover, weakly supervised learning driv- en by image attributes is performed to denoise the pseudo- labeled instances, thereby highlighting the unique strength of individual feature modality. Meanwhile, such learning can yield a few anchors in graphs that vitally enable the alignment and fusion of multiple graphs. As a result, an edge weight matrix learned from the fused graph automat- ically gives the ordering to the initially retrieved results. We evaluate our approach on four benchmark image re- trieval datasets, demonstrating a significant performance gain over the state-of-the-arts
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