Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Cutting-plane training of structural SVMs
Machine Learning
Exploiting known taxonomies in learning overlapping concepts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Articulated pose estimation with flexible mixtures-of-parts
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Proposal generation for object detection using cascaded ranking SVMs
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
On Taxonomies for Multi-class Image Categorization
International Journal of Computer Vision
Learning a category independent object detection cascade
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In computer vision efficient multi-class classification is becoming a key problem as the field develops and the number of object classes to be identified increases. Often objects might have some sort of structure such as a taxonomy in which the mis-classification score for object classes close by, using tree distance within the taxonomy, should be less than for those far apart. This is an example of multi-class classification in which the loss function has a special structure. Another example in vision is for the ubiquitous pictorial structure or parts based model. In this case we would like the mis-classification score to be proportional to the number of parts misclassified. It transpires both of these are examples of structured output ranking problems. However, so far no efficient large scale algorithm for this problem has been demonstrated. In this work we propose an algorithm for structured output ranking that can be trained in a time linear in the number of samples under a mild assumption common to many computer vision problems: that the loss function can be discretized into a small number of values. We show the feasibility of structured ranking on these two core computer vision problems and demonstrate a consistent and substantial improvement over competing techniques. Aside from this, we also achieve state-of-the art results for the PASCAL VOC human layout problem.