Multiple feature fusion by subspace learning
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
An efficient discriminant-based solution for small sample size problem
Pattern Recognition
Discriminative optical flow tensor for video semantic analysis
Computer Vision and Image Understanding
Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Locating nose-tips and estimating head poses in images by tensorposes
IEEE Transactions on Circuits and Systems for Video Technology
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
Discriminant subspace analysis: an adaptive approach for image classification
IEEE Transactions on Multimedia
Discriminative orthogonal neighborhood-preserving projections for classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Active reranking for web image search
IEEE Transactions on Image Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Tensor distance based multilinear locality-preserved maximum information embedding
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Volterra kernel based face recognition using artificial bee colonyoptimization
Engineering Applications of Artificial Intelligence
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Images, as high-dimensional data, usually embody large variabilities. To classify images for versatile applications, an effective algorithm is necessarily designed by systematically considering the data structure, similarity metric, discriminant subspace, and classifier. In this paper, we provide evidence that, besides the Fisher criterion, graph embedding, and tensorization used in many existing methods, the correlation-based similarity metric embodied in supervised multilinear discriminant subspace learning can additionally improve the classification performance. In particular, a novel discriminant subspace learning algorithm, called correlation tensor analysis (CTA), is designed to incorporate both graph-embedded correlational mapping and discriminant analysis in a Fisher type of learning manner. The correlation metric can estimate intrinsic angles and distances for the locally isometric embedding, which can deal with the case when Euclidean metric is incapable of capturing the intrinsic similarities between data points. CTA learns multiple interrelated subspaces to obtain a low-dimensional data representation reflecting both class label information and intrinsic geometric structure of the data distribution. Extensive comparisons with most popular subspace learning methods on face recognition evaluation demonstrate the effectiveness and superiority of CTA. Parameter analysis also reveals its robustness.