From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image Denoising Via Learned Dictionaries and Sparse representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
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
Geometric $/ell$_p-norm feature pooling for image classification
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
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Fisher Discrimination Dictionary Learning for sparse representation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Ask the locals: Multi-way local pooling for image recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A dictionary learning approach for classification: separating the particularity and the commonality
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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Empirically, we find that despite the most exclusively discriminative features owned by one specific object category, the various classes of objects usually share some common patterns, which do not contribute to the discrimination of them. Concentrating on this observation and motivated by the success of dictionary learning (DL) framework, in this paper, we propose to explicitly learn a class-specific dictionary (called particularity) for each category that captures the most discriminative features of this category, and simultaneously learn a common pattern pool (called commonality), whose atoms are shared by all the categories and only contribute to representation of the data rather than discrimination. In this way, the particularity differentiates the categories while the commonality provides the essential reconstruction for the objects. Thus, we can simply adopt a reconstruction-based scheme for classification. By reviewing the existing DL-based classification methods, we can see that our approach simultaneously learns a classification-oriented dictionary and drives the sparse coefficients as discriminative as possible. In this way, the proposed method will achieve better classification performance. To evaluate our method, we extensively conduct experiments both on synthetic data and real-world benchmarks in comparison with the existing DL-based classification algorithms, and the experimental results demonstrate the effectiveness of our method.