Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Epipolar Geometry Estimation via RANSAC Benefits from the Oriented Epipolar Constraint
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Introduction to Information Retrieval
Introduction to Information Retrieval
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Search by mobile image based on visual and spatial consistency
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
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The state-of-the-art mobile visual search approaches are based on the bag-of-visual-word (BoW). As BoW representation ignores geometric relationship among the local features, a full geometric constraint like RANSAC is usually used as a post-processing step to re-rank the matched images, which has been shown to greatly improve the precision but at high computational cost. In this paper we present a novel and efficient geometric re-ranking method. Our basic idea is that the true matching local features should be not only in a similar spatial context, but also have a consistent spatial relationship, thus we simultaneously introduce context similarity and spatial similarity to describe the geometric consistency. By incorporating these two geometric constraints, the co-occurring visual words in the same spatial context can be regarded as a "visual phrase"and significantly improve the discriminative power than single visual word. To evaluate our approach, we perform experiments on Star5k and ImageNet100k dataset. The comparison with the BoW method and Soft-assignment method highlights the effectiveness of our approach in both accuracy and speed.