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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
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Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Learning for Matrix Factorization and Sparse Coding
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Learning with l1-graph for image analysis
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-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
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Discriminative compact pyramids for object and scene recognition
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Modeling the spatial layout of images beyond spatial pyramids
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Accelerated large scale optimization by concomitant hashing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Auto-grouped sparse representation for visual analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Unsupervised and supervised visual codes with restricted boltzmann machines
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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Beyond spatial pyramid matching: spatial soft voting for image classification
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data into a significantly higher-dimensional space with sparse coding can lead to superior classification performance. However, computationally it is challenging to learn a set of highly over-complete dictionary bases and to encode the test data with the learned bases. In this paper, we describe a mixture sparse coding model that can produce high-dimensional sparse representations very efficiently. Besides the computational advantage, the model effectively encourages data that are similar to each other to enjoy similar sparse representations. What's more, the proposed model can be regarded as an approximation to the recently proposed local coordinate coding (LCC), which states that sparse coding can approximately learn the nonlinear manifold of the sensory data in a locally linear manner. Therefore, the feature learned by the mixture sparse coding model works pretty well with linear classifiers. We apply the proposed model to PASCAL VOC 2007 and 2009 datasets for the classification task, both achieving state-of-the-art performances.