VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
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
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
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video 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
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Embedding spatial information into image content description for scene retrieval
Pattern Recognition
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
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image retrieval with geometry-preserving visual phrases
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations
IEEE Transactions on Multimedia
On the Annotation of Web Videos by Efficient Near-Duplicate Search
IEEE Transactions on Multimedia
Contextual weighting for vocabulary tree based image retrieval
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Strong geometrical consistency in large scale partial-duplicate image search
Proceedings of the 21st ACM international conference on Multimedia
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Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visual-words model does not capture the geometric context among local features in images well, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing methods on global geometric verification are either computationally expensive to ensure real-time response, or cannot handle rotation well. To solve the preceding problems, in this article, we propose a novel geometric coding algorithm, to encode the spatial context among local features for large-scale partial-duplicate Web image retrieval. Our geometric coding consists of geometric square coding and geometric fan coding, which describe the spatial relationships of SIFT features into three geo-maps for global verification to remove geometrically inconsistent SIFT matches. Our approach is not only computationally efficient, but also effective in detecting partial-duplicate images with rotation, scale changes, partial-occlusion, and background clutter. Experiments in partial-duplicate Web image search, using two datasets with one million Web images as distractors, reveal that our approach outperforms the baseline bag-of-visual-words approach even following a RANSAC verification in mean average precision. Besides, our approach achieves comparable performance to other state-of-the-art global geometric verification methods, for example, spatial coding scheme, but is more computationally efficient.