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Visual recognition remains an extremely challenging problem in computer vision. Most previous approaches have been evaluated on small datasets. However, ImageNet dataset with millions images for thousands classes poses more challenges for the next generation of vision mechanisms. Learning an efficient visual classifier and constructing a robust visual representation in a large scale scenario are two main research issues. In this book, we present how to tackle these issues. Firstly, a novel approach is presented by using several local descriptors to improve the discriminative power of image representation. Secondly, the state-of-the-art SVMs are extended by building the balanced bagging classifiers with sampling strategy and parallelizing the training process with several multi-core computers. Thirdly, the binary stochastic gradient descent SVM is developed to the new multiclass SVM for efficiently classifying large image datasets into many classes. Finally, when the training data cannot fit into computer memory, the training task of SVM becomes more complicated to deal with. This challenge is addressed by an incremental learning method for both large scale linear and nonlinear SVMs
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Visual recognition remains an extremely challenging problem in computer vision. Most previous approaches have been evaluated on small datasets. However, ImageNet dataset with millions images for thousands classes poses more challenges for the next generation of vision mechanisms. Learning an efficient visual classifier and constructing a robust visual representation in a large scale scenario are two main research issues. In this book, we present how to tackle these issues. Firstly, a novel approach is presented by using several local descriptors to improve the discriminative power of image representation. Secondly, the state-of-the-art SVMs are extended by building the balanced bagging classifiers with sampling strategy and parallelizing the training process with several multi-core computers. Thirdly, the binary stochastic gradient descent SVM is developed to the new multiclass SVM for efficiently classifying large image datasets into many classes. Finally, when the training data cannot fit into computer memory, the training task of SVM becomes more complicated to deal with. This challenge is addressed by an incremental learning method for both large scale linear and nonlinear SVMs
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Large Scale Support Vector Machines Algorithms for Visual Recognition
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Doan Thanh-NghiThanh-Nghi Doan received his Doctorate degree in computer science from University of Rennes 1, France, 2013. He was working as a Ph.D. candidate in TEXMEX Research Team, IRISA, France. Currently, he is working at An Gi. Bestandsnummer des Verkäufers 151398522
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Large Scale Support Vector Machines Algorithms for Visual Recognition
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Visual recognition remains an extremely challenging problem in computer vision. Most previous approaches have been evaluated on small datasets. However, ImageNet dataset with millions images for thousands classes poses more challenges for the next generation of vision mechanisms. Learning an efficient visual classifier and constructing a robust visual representation in a large scale scenario are two main research issues. In this book, we present how to tackle these issues. Firstly, a novel approach is presented by using several local descriptors to improve the discriminative power of image representation. Secondly, the state-of-the-art SVMs are extended by building the balanced bagging classifiers with sampling strategy and parallelizing the training process with several multi-core computers. Thirdly, the binary stochastic gradient descent SVM is developed to the new multiclass SVM for efficiently classifying large image datasets into many classes. Finally, when the training data cannot fit into computer memory, the training task of SVM becomes more complicated to deal with. This challenge is addressed by an incremental learning method for both large scale linear and nonlinear SVMsVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Englisch. Bestandsnummer des Verkäufers 9783639715750
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Large Scale Support Vector Machines Algorithms for Visual Recognition
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Visual recognition remains an extremely challenging problem in computer vision. Most previous approaches have been evaluated on small datasets. However, ImageNet dataset with millions images for thousands classes poses more challenges for the next generation of vision mechanisms. Learning an efficient visual classifier and constructing a robust visual representation in a large scale scenario are two main research issues. In this book, we present how to tackle these issues. Firstly, a novel approach is presented by using several local descriptors to improve the discriminative power of image representation. Secondly, the state-of-the-art SVMs are extended by building the balanced bagging classifiers with sampling strategy and parallelizing the training process with several multi-core computers. Thirdly, the binary stochastic gradient descent SVM is developed to the new multiclass SVM for efficiently classifying large image datasets into many classes. Finally, when the training data cannot fit into computer memory, the training task of SVM becomes more complicated to deal with. This challenge is addressed by an incremental learning method for both large scale linear and nonlinear SVMs. Bestandsnummer des Verkäufers 9783639715750
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Large Scale Support Vector Machines Algorithms for Visual Recognition
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Visual recognition remains an extremely challenging problem in computer vision. Most previous approaches have been evaluated on small datasets. However, ImageNet dataset with millions images for thousands classes poses more challenges for the next generation of vision mechanisms. Learning an efficient visual classifier and constructing a robust visual representation in a large scale scenario are two main research issues. In this book, we present how to tackle these issues. Firstly, a novel approach is presented by using several local descriptors to improve the discriminative power of image representation. Secondly, the state-of-the-art SVMs are extended by building the balanced bagging classifiers with sampling strategy and parallelizing the training process with several multi-core computers. Thirdly, the binary stochastic gradient descent SVM is developed to the new multiclass SVM for efficiently classifying large image datasets into many classes. Finally, when the training data cannot fit into computer memory, the training task of SVM becomes more complicated to deal with. This challenge is addressed by an incremental learning method for both large scale linear and nonlinear SVMs 164 pp. Englisch. Bestandsnummer des Verkäufers 9783639715750
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Large Scale Support Vector Machines Algorithms for Visual Recognition
Verlag:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3639715756
ISBN 13: 9783639715750
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Zustand: New. pp. 164. Bestandsnummer des Verkäufers 26128106464
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Large Scale Support Vector Machines Algorithms for Visual Recognition
Verlag:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3639715756
ISBN 13: 9783639715750
Neu
Softcover
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Zustand: New. PRINT ON DEMAND pp. 164. Bestandsnummer des Verkäufers 18128106474
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Beispielbild für diese ISBN
Large Scale Support Vector Machines Algorithms for Visual Recognition
Verlag:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3639715756
ISBN 13: 9783639715750
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Softcover
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Zustand: New. Print on Demand pp. 164 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Bestandsnummer des Verkäufers 131399743
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