1. Node-Sensitive Graph Fusion via Topo-Correlation for Image Retrieval.
- Author
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Li, Ying, Kong, Xiangwei, Fu, Haiyan, and Tian, Qi
- Subjects
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IMAGE retrieval , *CONTENT-based image retrieval , *WEIGHTED graphs , *COMPUTATIONAL complexity , *BINARY codes - Abstract
Various kinds of features prove to be effective for content-based image retrieval. However, due to the diversity of image contents, a descriptor may achieve impressive performance on specific images while becoming invalid on others. Although some efforts have been made to combine features as complementary counterparts, proper weighting scheme is still a challenge for fast and accurate retrieval. In this paper, we propose an effective fusion method, termed as Topo-correlation (Topo), where the importance of each feature is measured by cross-view correlations on local affinity graphs. Specifically, the weights of similarities are node-sensitive as well as modality-sensitive, thus boosting the results of good cues while depressing adverse factors for individual images. By estimating the consensus of similarity scores with regard to a query-driven criterion, the weighted graphs are generated efficiently with low computational complexity. Extensive experimental results on four benchmarks demonstrate the superiority of the proposed approach over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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