Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
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
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - 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
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative
Proceedings of the international conference on Multimedia information retrieval
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
Large scale image search with geometric coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
On the Annotation of Web Videos by Efficient Near-Duplicate Search
IEEE Transactions on Multimedia
SIFT match verification by geometric coding for large-scale partial-duplicate web image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Hi-index | 0.00 |
The state-of-the-art partial-duplicate image search systems reply heavily on the match of local features like SIFT. Independently matching local features across two images ignores the overall geometry structure and therefore may incur many false matches. To reduce such matches, several geometry verification methods have been proposed. This paper introduces a new geometry verification method named as Strong Geometry Consistency (SGC), which uses the orientation, scale and location information of the local feature points to accurately and quickly remove the false matches. We also propose a simple scale weighting (SW) strategy, which gives feature points with larger scales greater weights, based on the intuition that a larger-scale feature point tends to be more robust for image search as it occupies a larger area of an image. Extensive experiments performed on three popular datasets show that SGC significantly outperforms state-of-the-art geometry verification methods, and SW can further boost the performance with marginal additional computation.