Personalized portraits ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
RGB-D based multi-attribute people search in intelligent visual surveillance
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Color fused multiple features for traffic sign recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Proceedings of the 20th ACM international conference on Multimedia
Attribute-assisted reranking for web image retrieval
Proceedings of the 20th ACM international conference on Multimedia
Describing clothing by semantic attributes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Image retrieval with structured object queries using latent ranking SVM
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Towards person identification and re-identification with attributes
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Recognizing complex events using large margin joint low-level event model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
Learning attribute relation in attribute-based zero-shot classification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Relative forest for attribute prediction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Efficient clothing retrieval with semantic-preserving visual phrases
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
ObjectPatchNet: Towards scalable and semantic image annotation and retrieval
Computer Vision and Image Understanding
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We propose a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multiword queries. Moreover, these approaches also ignore the interdependencies among the query terms. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval. Furthermore, we integrate ranking and retrieval within the same formulation, by posing them as structured prediction problems. Extensive experimental evaluation on the Labeled Faces in the Wild(LFW), FaceTracer and PASCAL VOC datasets show that our approach significantly outperforms several state-of-the-art ranking and retrieval methods.