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
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Ranking Model Adaptation for Domain-Specific Search
IEEE Transactions on Knowledge and Data Engineering
Exploiting visual word co-occurrence for image retrieval
Proceedings of the 20th ACM international conference on Multimedia
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The well-known bag-of-features (BoF) model is widely utilized for large scale image retrieval. However, BoF model lacks the spatial information of visual words, which is informative for local features to build up meaningful visual patches. To compensate for the spatial information loss, in this paper, we propose a novel query expansion method called Spatial Co-occurrence Query Expansion (SCQE), by utilizing the spatial co-occurrence information of visual words mined from the database images to boost the retrieval performance. In offline phase, for each visual word in the vocabulary, we treat the visual words that are frequently co-occurred with it in the database images as neighbors, base on which a spatial co-occurrence graph is built. In online phase, a query image can be expanded with some spatial co-occurred but unseen visual words according to the spatial co-occurrence graph, and the retrieval performance can be improved by expanding these visual words appropriately. Experimental results demonstrate that, SCQE achieves promising improvements over the typical BoF baseline on two datasets comprising 5K and 505K images respectively.