Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Automatic attribute discovery and characterization from noisy web data
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Attribute-based transfer learning for object categorization with zero/one training example
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A discriminative latent model of object classes and attributes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Interactively building a discriminative vocabulary of nameable attributes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Actively selecting annotations among objects and attributes
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A joint learning framework for attribute models and object descriptions
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Unsupervised learning of relative visual attributes is important because it is often infeasible for a human annotator to predefine and manually label all the relative attributes in large datasets. We propose a method for learning relative visual attributes given a set of images for each training class. The method is unsupervised in the sense that it does not require a set of predefined attributes. We formulate the learning as a mixed-integer programming problem and propose an efficient algorithm to solve it approximately. Experiments show that the learned attributes can provide good generalization and tend to be more discriminative than hand-labeled relative attributes. While in the unsupervised setting the learned attributes do not have explicit names, many are highly correlated with human annotated attributes and this demonstrates that our method is able to discover relative attributes automatically.