Image annotation with tagprop on the MIRFLICKR set
Proceedings of the international conference on Multimedia information retrieval
Co-transduction for shape retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Fast shape re-ranking with neighborhood induced similarity measure
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Context-Aware Semi-Local Feature Detector
ACM Transactions on Intelligent Systems and Technology (TIST)
Exploiting visual word co-occurrence for image retrieval
Proceedings of the 20th ACM international conference on Multimedia
Query specific fusion for image retrieval
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Size matters: exhaustive geometric verification for image retrieval accepted for ECCV 2012
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Negative evidences and co-occurences in image retrieval: the benefit of PCA and whitening
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Densifying Distance Spaces for Shape and Image Retrieval
Journal of Mathematical Imaging and Vision
Image re-ranking and rank aggregation based on similarity of ranked lists
Pattern Recognition
Sim-min-hash: an efficient matching technique for linking large image collections
Proceedings of the 21st ACM international conference on Multimedia
Local and global scaling reduce hubs in space
The Journal of Machine Learning Research
RankCNN: When learning to rank encounters the pseudo preference feedback
Computer Standards & Interfaces
On the mutual nearest neighbors estimate in regression
The Journal of Machine Learning Research
Image and Vision Computing
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This paper introduces the contextual dissimilarity measure, which significantly improves the accuracy of bag-of-features-based image search. Our measure takes into account the local distribution of the vectors and iteratively estimates distance update terms in the spirit of Sinkhorn's scaling algorithm, thereby modifying the neighborhood structure. Experimental results show that our approach gives significantly better results than a standard distance and outperforms the state of the art in terms of accuracy on the Nist茅r-Stew茅nius and Lola data sets. This paper also evaluates the impact of a large number of parameters, including the number of descriptors, the clustering method, the visual vocabulary size, and the distance measure. The optimal parameter choice is shown to be quite context-dependent. In particular, using a large number of descriptors is interesting only when using our dissimilarity measure. We have also evaluated two novel variants: multiple assignment and rank aggregation. They are shown to further improve accuracy at the cost of higher memory usage and lower efficiency.