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
Vector Boosting for Rotation Invariant Multi-View Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative mixture-of-templates for viewpoint classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Efficient Additive Kernels via Explicit Feature Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
A coarse-to-fine approach for fast deformable object detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Reconfigurable models for scene recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ensemble of exemplar-SVMs for object detection and beyond
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The non-linear decision boundary between object and background classes - due to large intra-class variations - needs to be modelled by any classifier wishing to achieve good results. While a mixture of linear classifiers is capable of modelling this non-linearity, learning this mixture from weakly annotated data is non-trivial and is the paper's focus. Our approach is to identify the modes in the distribution of our positive examples by clustering, and to utilize this clustering in a latent SVM formulation to learn the mixture model. The clustering relies on a robust measure of visual similarity which suppresses uninformative clutter by using a novel representation based on the exemplar SVM. This subtle clustering of the data leads to learning better mixture models, as is demonstrated via extensive evaluations on Pascal VOC 2007. The final classifier, using a HOG representation of the global image patch, achieves performance comparable to the state-of-the-art while being more efficient at detection time.