Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Geometric Hashing with Local Affine Frames
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Efficient Visual Search of Videos Cast as Text Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
A low-dimensional local descriptor incorporating TPS warping for image matching
Image and Vision Computing
Large vocabularies for keypoint-based representation and matching of image patches
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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Detection of similar fragments in unknown images is typically based on the hypothesize-and-verify paradigm. After the keypoint correspondences are found, the configuration constraints are used to identify clusters of similar and similarly transformed keypoints. This method is computationally expensive and hardly applicable to large databases. As an alternative, we propose novel affine-invariant TERM features characterizing geometry of groups of elliptical keyregions so that similar patches can be found by feature matching only. The paper overviews TERM features and reports experimental results confirming their high performances in image matching. A method combining visual words based on TERM descriptors with SIFT words is particularly recommended. Because of its low complexity, the proposed method can be prospectively used with visual databases of large sizes.