Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Atomic Decomposition by Basis Pursuit
SIAM Review
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval
Computer Vision and Image Understanding
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Proceedings of the Tenth International Workshop on Multimedia Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Action Recognition from One Example
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-manifold discriminant analysis method for image feature extraction
Pattern Recognition
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
SIAM Journal on Scientific Computing
Fast Solution of -Norm Minimization Problems When the Solution May Be Sparse
IEEE Transactions on Information Theory
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Face Verification Using the LARK Representation
IEEE Transactions on Information Forensics and Security
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Image feature extraction is one of the most important problems for image recognition system. We tackle this by combing the locally adaptive regression kernel descriptors (LARK), bag-of-visual-words and sparse representation. Specifically, this paper makes two main contributions: (1) we introduce a novel method called histogram of visual words based on locally adaptive regression kernels descriptors (HWLD) for image feature extraction. LARK is used to describe the image local information and build the visual vocabulary. Each pixel of an image is assigned to the visual words and gets the corresponding weights. Image feature vector is obtained by subdividing the image and computing the accumulative weight histograms of visual words in these sub-blocks. (2) The K nearest neighbor based sparse representation (KNN-SR) is presented for assigning the visual words. Compared with nearest neighbors based method, KNN-SR has stronger discriminant power to identify different patches in the image. Experimental results on the AR face image set, the CMU-PIE face image set, the ETH80 object image set and the Nister image set demonstrate that our method is more effective than some state-of-the-art feature extraction methods.