Modern Information Retrieval
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
Vector field learning via spectral filtering
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Point Set Registration: Coherent Point Drift
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-up, relaxed spatial matching
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Establishing correct correspondences between two images has a wide range of applications, such as 2D and 3D registration, structure from motion, and image retrieval. In this paper, we propose a new matching method based on spatial constraints. The proposed method has linear time complexity, and is efficient when applying it to image retrieval. The main assumption behind our method is that, the local geometric structure among a feature point and its neighbors, is not easily affected by both geometric and photometric transformations, and thus should be preserved in their corresponding images. We model this local geometric structure by linear coefficients that reconstruct the point from its neighbors. The method is flexible, as it can not only estimate the number of correct matches between two images efficiently, but also determine the correctness of each match accurately. Furthermore, it is simple and easy to be implemented. When applying the proposed method on re-ranking images in an image search engine, it outperforms the-state-of-the-art techniques.