A non-rigid appearance model for shape description and recognition
Pattern Recognition
Point-context descriptor based region search for logo recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Proceedings of the 20th ACM international conference on Multimedia
Attributes for classifier feedback
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Proceedings of the 21st ACM international conference on Multimedia
ObjectPatchNet: Towards scalable and semantic image annotation and retrieval
Computer Vision and Image Understanding
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Attributes were recently shown to give excellent results for category recognition. In this paper, we demonstrate their performance in the context of image retrieval. First, we show that retrieving images of particular objects based on attribute vectors gives results comparable to the state of the art. Second, we demonstrate that combining attribute and Fisher vectors improves performance for retrieval of particular objects as well as categories. Third, we implement an efficient coding technique for compressing the combined descriptor to very small codes. Experimental results on the Holidays dataset show that our approach significantly outperforms the state of the art, even for a very compact representation of 16 bytes per image. Retrieving category images is evaluated on the "web-queries" dataset. We show that attribute features combined with Fisher vectors improve the performance and that combined image features can supplement text features.