An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Building contextual visual vocabulary for large-scale image applications
Proceedings of the international conference on Multimedia
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
Large scale image search with geometric coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Image retrieval with geometry-preserving visual phrases
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Contextual weighting for vocabulary tree based image retrieval
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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One most popular approach for large-scale content-based image retrieval is based on the Bag-of-Visual-Words model. Since the spatial context among local features is very important for visual content identification, many approaches index local features' geometric clues, such as location, scale and orientation for post-verification. To obtain consistent accuracy performance, the amount of top ranked images that post-verification approach needs to process is proportional to the image database size. When the database is very large, the verified images will be too many to be processed in real-time response. To address this issue, in this paper, we explore two approaches to embed spatial context information into the inverted file. The first one is to build a spatial relationship dictionary embedded with spatial context among local features, which we call one-one spatial relationship method. The second one is to generate a spatial context binary signature for each feature, which we call one-multiple spatial relationship method. Then we build an inverted file with spatial information between local features. The geometric verification is implicitly achieved while traversing the inverted file. Experimental results on benchmark Holidays dataset demonstrate the efficiency of the proposed algorithm.