Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Discriminative Object Class Models of Appearance and Shape by Correlatons
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Computer Vision and Image Understanding
Multilevel Image Coding with Hyperfeatures
International Journal of Computer Vision
Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval
Computer Vision and Image Understanding
Spatial Hierarchy of Textons Distributions for Scene Classification
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video Retrieval
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A performance evaluation of gradient field HOG descriptor for sketch based image retrieval
Computer Vision and Image Understanding
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Bag-of-visual words (BOVW) is a local feature based framework for content-based image and video retrieval. Its performance relies on the discriminative power of visual vocabulary, i.e. the cluster set on local features. However, the optimisation of visual vocabulary is of a high complexity in a large collection. This paper aims to relax such a dependence by adapting the query generative model to BOVW based retrieval. Local features are directly projected onto latent content topics to create effective visual queries; visual word distributions are learnt around local features to estimate the contribution of a visual word to a query topic; the relevance is justified by considering concept distributions on visual words as well as on local features. Massive experiments are carried out the TRECVid 2009 collection. The notable improvement on retrieval performance shows that this probabilistic framework alleviates the problem of visual ambiguity and is able to afford visual vocabulary with relatively low discriminative power.