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This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
List of contents
Active Shape Model and Its Application to Face Alignment.-
Condition Relaxation in Conditional Statistical Shape Models.-
Independent Component Analysis and Its Application to Classification of High-Resolution Remote Sensing Images.-
Subspace Construction from Artificially Generated Images for Traffic Sign Recognition.-
Local Structure Preserving based Subspace Analysis Methods and Applications.-
Sparse Representation for Image Super-Resolution.-
Sampling andRecovery of Continuously-Defined Sparse Signals and Its Applications.-
Tensor-Based Subspace Learning for Multi-Pose Face Synthesis.
Summary
This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
