Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Predicting Structured Data (Neural Information Processing)
Predicting Structured Data (Neural Information Processing)
Comparative study of metrics for evaluation of object localisation by bounding boxes.
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Techniques for still image scene classification and object detection
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Active Structured Learning for High-Speed Object Detection
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Backprojection revisited: scalable multi-view object detection and similarity metrics for detections
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Optimizing complex loss functions in structured prediction
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
On parameter learning in CRF-based approaches to object class image segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Efficient structured support vector regression
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Fast PRISM: Branch and Bound Hough Transform for Object Class Detection
International Journal of Computer Vision
Branch and bound strategies for non-maximal suppression in object detection
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Discriminative Models for Multi-Class Object Layout
International Journal of Computer Vision
Exploiting context aware category discovery for image labeling
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Structured Output SVM for Remote Sensing Image Classification
Journal of Signal Processing Systems
Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness
SIAM Journal on Imaging Sciences
Co-occurrence random forests for object localization and classification
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Object Recognition by Sequential Figure-Ground Ranking
International Journal of Computer Vision
Taxonomic multi-class prediction and person layout using efficient structured ranking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Robust tracking with weighted online structured learning
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Latent pyramidal regions for recognizing scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
3D2PM - 3d deformable part models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Joint kernel learning for supervised image segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Class-Specified segmentation with multi-scale superpixels
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Inductive manifold learning using structured support vector machine
Pattern Recognition
Learning discriminative localization from weakly labeled data
Pattern Recognition
Robotics and Autonomous Systems
Regressing Local to Global Shape Properties for Online Segmentation and Tracking
International Journal of Computer Vision
Branch&Rank for Efficient Object Detection
International Journal of Computer Vision
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Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. The use of a joint-kernelframework allows us to formulate the training procedure as a generalization of an SVM, which can be solved efficiently. We further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. Experimental evaluation on the PASCAL VOC and TU Darmstadt datasets show that the structured training procedure improves performance over binary training as well as the best previously published scores.