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Reranking is one of the commonly used schemes to improve the initial ranking performance for content based image retrieval (CBIR). The state-of-the-art reranking methods for CBIR are mainly based on spatial verification and global feature. To mine the complementary properties of different reranking strategies, we combine features representing images from different perspectives with RankSVM to obtain...
With the recent advancement of web search ranking framework, a.k.a. learning to rank, it is questionable whether it can be still applicable to the large-scale content based image retrieval settings. Moreover, given the complex structure of image representation, it is also challenging how to design visual ranking features that not only scale up well, but also model various visual modalities and the...
Existing image retrieval systems suffer from a performance variance for different queries. Severe performance variance may greatly degrade the effectiveness of the subsequent query-dependent ranking optimization algorithms, especially those that utilize the information mined from the initial search results. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval...
Along with the success of "bag of visual words" scheme in content based image retrieval (CBIR), various technologies in text information retrieval realm have been transferred into image retrieval system and obtain promising performance. However, how to select the suitable ranking technology, such as ranking model, for a specific image database is still an open question. Because most ranking...
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