Tikhonov Regularization and Total Least Squares
SIAM Journal on Matrix Analysis and Applications
Contextual Priming for Object Detection
International Journal of Computer Vision
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
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution models for object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Discriminative Models for Multi-Class Object Layout
International Journal of Computer Vision
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Recognition using visual phrases
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Sparse Representation for Color Image Restoration
IEEE Transactions on Image Processing
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Segmentation as selective search for object recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Recent successes in the use of sparse coding for many computer vision applications have triggered the attention towards the problem of how an over-complete dictionary should be learned from data. This is because the quality of a dictionary greatly affects performance in many respects, including computational. While so far the focus has been on learning compact, reconstructive, and discriminative dictionaries, in this work we propose to retain the previous qualities, and further enhance them by learning a dictionary that is able to predict the contextual information surrounding a sparsely coded signal. The proposed framework leverages the K-SVD for learning, fully inheriting its benefits of simplicity and efficiency. A model of structured prediction is designed around this approach, which leverages contextual information to improve the combined recognition and localization of multiple objects from multiple classes within one image. Results on the PASCAL VOC 2007 dataset are in line with the state-of-the-art, and clearly indicate that this is a viable approach for learning a context aware dictionary for sparse representation.