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Query Adaptive Similarity for Large Scale Object Retrieval

D. Qin, C. Wengert and L. Van Gool
26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013)
June 2013

Abstract

Many recent object retrieval systems rely on local features for describing an image. The similarity between a pair of images is measured by aggregating the similarity between their corresponding local features. In this paper we present a probabilistic framework for modeling the feature to feature similarity measure. We then derive a query adaptive distance which is appropriate for global similarity evaluation. Furthermore, we propose a function to score the individual contributions into an image to image similarity within the probabilistic framework. Experimental results show that our method improves the retrieval accuracy significantly and consistently. Moreover, our result compares favorably to the state-of-the-art.


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@InProceedings{eth_biwi_01026,
  author = {D. Qin and C. Wengert and L. Van Gool},
  title = {Query Adaptive Similarity for Large Scale Object Retrieval},
  booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013)},
  year = {2013},
  month = {June},
  keywords = {}
}