Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Proceedings of the 12th annual ACM international conference on Multimedia
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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
On the Annotation of Web Videos by Efficient Near-Duplicate Search
IEEE Transactions on Multimedia
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State-of-the-art image search systems mostly build on bag-of-features (BOF) representation. As BOF ignores geometric relationships among local features, geometric consistency constraints have been proposed to improve search precision. However, exploiting full geometric constraints are too computational expensive. Weak geometric constraints have strong assumptions and can only deal with uniform transformations. To handle view point changes and nonrigid deformations, in this paper we present a novel pairwise weak geometric consistency constraint (P-WGC) method. It utilizes the local similarity characteristic of deformations, and measures the pairwise geometric similarity of matches between two sets of local features. Experiments performed on four famous datasets and a dataset of one million of images show a significant improvement due to P-WGC as well as its efficiency. Further improvement of search accuracy is obtained when it is combined with full geometric verification.