Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Image annotation: which approach for realistic databases?
Proceedings of the 6th ACM international conference on Image and video retrieval
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Efficient object-class recognition by boosting contextual information
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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The bag-of-visual-words is a popular representation for images that has proven to be quite effective for automatic annotation. In this paper, we extend this representation in order to include weak geometrical information by using visual word pairs. We show on a standard benchmark dataset that this new image representation improves significantly the performances of an automatic annotation system.