Weakly supervised learning of component-based hierarchical model for object detection
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Spatial-temporal granularity-tunable gradients partition (STGGP) descriptors for human detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Object classification using heterogeneous co-occurrence features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Multi-class classification on Riemannian manifolds for video surveillance
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Multiresolution models for object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Object recognition using junctions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Object classification using heterogeneous co-occurrence features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A structural filter approach to human detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Recursive coarse-to-fine localization for fast object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Figure-ground image segmentation helps weakly-supervised learning of objects
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
A heuristic deformable pedestrian detection method
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Self-tuned Evolution-COnstructed features for general object recognition
Pattern Recognition
A multiple component matching framework for person re-identification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
On Taxonomies for Multi-class Image Categorization
International Journal of Computer Vision
Contextual object detection using set-based classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
A feature construction method for general object recognition
Pattern Recognition
A novel online boosting algorithm for automatic anatomy detection
Machine Vision and Applications
Hi-index | 0.00 |
Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, achieving very low false positives rates. The field has also seen a resurgence of part-based recognition methods, with impressive results on highly articulated, diverse object categories. In this paper we propose a discriminative learning approach for detection that is inspired by part-based recognition approaches. Our method, Multiple Component Learning (mcl), automatically learns individual component classifiers and combines these into an overall classifier. Unlike previous methods, which rely on either fairly restricted part models or labeled part data, mcl learns powerful component classifiers in a weakly supervised manner, where object labels are provided but part labels are not. The basis of mcl lies in learning a set classifier; we achieve this by combining boosting with weakly supervised learning, specifically the Multiple Instance Learning framework (mil). mcl is general, and we demonstrate results on a range of data from computer audition and computer vision. In particular, mcl outperforms all existing methods on the challenging INRIA pedestrian detection dataset, and unlike methods that are not part-based, mcl is quite robust to occlusions.