Pairwise Geometric Matching for Large-Scale Object Retrieval

Xinchao Li, Martha Larson, Alan Hanjalic; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5153-5161

Abstract


Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. In this paper, we consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. In addition, we combine the information on geometric relations from both the individual correspondences and pairs of correspondences to further improve the verification accuracy. Experimental results on three reference datasets show that the proposed approach results in a substantial performance improvement compared to the existing methods, without making concessions regarding computational efficiency.

Related Material


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[bibtex]
@InProceedings{Li_2015_CVPR,
author = {Li, Xinchao and Larson, Martha and Hanjalic, Alan},
title = {Pairwise Geometric Matching for Large-Scale Object Retrieval},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}