Shape quantization and recognition with randomized trees
Neural Computation
Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Semantic label sharing for learning with many categories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Ordinal hyperplanes ranker with cost sensitivities for age estimation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image ranking and retrieval based on multi-attribute queries
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
IEEE Transactions on Image Processing
SUN attribute database: Discovering, annotating, and recognizing scene attributes
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Human action recognition by learning bases of action attributes and parts
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Describing people: A poselet-based approach to attribute classification
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Human-Namable visual attributes are promising in leveraging various recognition tasks. Intuitively, the more accurate the attribute prediction is, the more the recognition tasks can benefit. Relative attributes [1] learns a ranking function per attribute which can provide more accurate attribute prediction, thus, show clear advantages over previous binary attribute. In this paper, we inherit the idea of learning ranking function per attribute but propose to improve the algorithm in two aspects: First, we propose a Relative Tree algorithm which facilitates more accurate nonlinear ranking to capture the semantic relationships. Second, we develop a Relative Forest algorithm which resorts to randomized learning to reduce training time of Relative Tree. Benefiting from multiple tree ensemble, Relative Forest can achieve even more accurate final ranking. To show the effectiveness of proposed method, we first compare Relative Tree method with Relative Attribute on PubFig and OSR dataset. Then to verify the efficiency of Relative Forest algorithm, we conduct age estimation evaluation on FG-NET dataset. With much less training time compared to Relative Attribute and Relative Tree, proposed Relative Forest achieves state-of-the-art age estimation accuracy. Finally, experiments on the large scale SUN Attribute database show the scalability of proposed Relative Forest.