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Machine Learning for Vision-Based Motion Analysis Theory and Techniques

:	Liang Wang, Guoying Zhao, Li Cheng, Matti Pietikäinen
:	Liang Wang, Guoying Zhao, Li Cheng, Matti Pietikäinen
:	Machine Learning for Vision-Based Motion Analysis Theory and Techniques
:	Springer-Verlag
:	9780857290571
:	Advances in Computer Vision and Pattern Recognition
:	1
:	CHF 135.30
:

:	Anwendungs-Software
:	English

:	372
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition.

Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions.

Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets.

Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.

	Preface	5
	Part I: Manifold Learning and Clustering/Segmentation	6
	Part II: Tracking	7
	Part III: Motion Analysis and Behavior Modeling	9
	Part IV: Gesture and Action Recognition	10
	Acknowledgements	11
	Contents	12
	Manifold Learning and Clustering/Segmentation	14
	Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis	15
	Introduction	15
	Spectral Clustering	16
	Principle	16
	Algorithm	18
	Related Work	18
	Dimensionality Reduction by Random Projection	19
	Random Projection	19
	Acceleration of Kernel Computation	20
	Random Sampling as Random Projection	20
	Using a Minority of Image Pixels	21
	Efficient Random Projection	21
	Size Reduction of Affinity Matrix by Sampling	23
	Random Subsampling	24
	Pre-clustering	25
	Practical Ncut Algorithms	26
	Randomized Ncut Algorithm	26
	Invocation of Dimensionality Reduction	27
	Relation to the Original Algorithm	27
	Scale Selection	27
	Number of Clusters	28
	Ncut Algorithm with Pre-clustering	28
	Experiments	29
	Performance Tests	29
	Error Analysis	29
	Computational Cost	30
	Image Segmentation	31
	Motion Segmentation	32
	Video Shot Segmentation	33
	Segmentation Using Appearance-Based Similarities	33
	Segmentation with Local Scaling	33
	Conclusions	35
	Appendix: Clustering Scores	37
	References	37
	Riemannian Manifold Clustering and Dimensionality Reduction for Vision-Based Analysis	39
	Introduction	40
	Chapter summary	42
	Review of Local Nonlinear Dimensionality Reduction Methods in Euclidean Spaces	43
	NLDR for a Nonlinear Manifold	43
	Calculation of M in LLE	44
	Calculation of M in LE	44
	Calculation of M in HLLE	45
	NLDR for a Single Subspace	45
	Manifold Clustering and Dimensionality Reduction Using the Euclidean Metric	47
	Manifold Clustering and Dimensionality Reduction for a k-Separated Union of k-Connected Nonlinear Manifolds	47
	Degeneracies for a k-Separated Union of k-Connected Linear Manifolds	48
	Manifold Clustering and Dimensionality Reduction Using the Riemannian Metric	50
	Review of Riemannian Manifolds	50
	Extending Manifold Clustering and Dimensionality Reduction to Riemannian Manifolds	53
	Selection of the Riemannian kNN	53
	Riemannian Calculation of M for LLE	53
	Riemannian Calculation of M for LE	54
	Riemannian Calculation of M for HLLE	54
	Calculation of the Embedding Coordinates	54
	Extending Manifold Clustering to Riemannian Manifolds	55
	Experiments	55
	Application and Experiments on SPSD(3)	55
	Application and Experiments on the Space of Probability Density Functions	58
	Conclusion and Open Research Problems	62
	References	63
	Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models	66
	Introduction	66
	Learning Pullback Metrics for Linear Models	68
	Pullback Metrics	68
	Fisher Metric for Linear Models	69
	General Framework	69
	Objective Functions: Classification Performance and Inverse Volume	71
	Pullback Metrics for Multidimensional Autoregressive Models	72
	The Basis Manifold	72
	The Basis Manifold AR(2,1) in the Scalar Case	72
	The Multidimensional Case	73
	Product Metric	73
	Geodesics	74
	An Automorphism for the Scalar Case	75
	Product and Global Automorphisms for AR(2,p)	75