Large Scale Support Vector Machines Algorithms for Visual Recognition - Couverture souple

Doan, Thanh-Nghi; Poulet, Francois

9783639715750: Large Scale Support Vector Machines Algorithms for Visual Recognition

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ISBN 10 : 3639715756 ISBN 13 : 9783639715750

Editeur : Scholars' Press, 2014

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La reconnaissance visuelle reste un probl�me extr�mement difficile en vision par ordinateur. La plupart des approches pr�c�dentes ont �t� �valu�es sur de petits ensembles de donn�es. Cependant, l'ensemble de donn�es ImageNet avec des millions d'images pour des milliers de classes pose plus de d�fis pour la prochaine g�n�ration de m�canismes de vision. Apprendre un classificateur visuel efficace et construire une repr�sentation visuelle robuste dans un sc�nario � grande �chelle sont deux principaux probl�mes de recherche. Dans ce livre, nous pr�sentons comment aborder ces probl�mes. Tout d'abord, une nouvelle approche est pr�sent�e en utilisant plusieurs descripteurs locaux pour am�liorer le pouvoir discriminatif de la repr�sentation de l'image. Deuxi�mement, les SVM de pointe sont �tendues en construisant des classificateurs d'ensachage �quilibr�s avec une strat�gie d'�chantillonnage et en parall�lisant le processus de formation avec plusieurs ordinateurs multicœurs. Troisi�mement, la SVM de descente de gradient stochastique binaire est d�velopp�e pour la nouvelle SVM multiclass pour classer efficacement les grands ensembles de donn�es d'image dans de nombreuses classes. Enfin, lorsque les donn�es d'entra�nement ne peuvent pas rentrer dans la m�moire de l'ordinateur, la t�che de formation de SVM devient plus compliqu�e � g�rer. Ce d�fi est r�solu par une m�thode d'apprentissage incr�mental pour les SVM lin�aires et non lin�aires � grande �chelle

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Scholars' Press
Date d'�dition: 2014
Langue: anglais
ISBN 10: 3639715756
ISBN 13: 9783639715750
Reliure: Broch�
Nombre de pages: 164
Coordonn�es du fabricant: non disponible
Personne responsable: non disponible

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Large Scale Support Vector Machines Algorithms for Visual Recognition

Thanh-Nghi Doan|Francois Poulet

Edit� par Scholars\' Press, 2014

ISBN 10 : 3639715756 ISBN 13 : 9783639715750

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Etat : 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. N� de r�f. du vendeur 151398522

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Large Scale Support Vector Machines Algorithms for Visual Recognition

Thanh-Nghi Doan

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783639715750

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Large Scale Support Vector Machines Algorithms for Visual Recognition

Thanh-Nghi Doan

Edit� par Scholars' Press Mai 2014, 2014

ISBN 10 : 3639715756 ISBN 13 : 9783639715750

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783639715750

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Large Scale Support Vector Machines Algorithms for Visual Recognition

Thanh-Nghi Doan

Edit� par Scholars' Press Mai 2014, 2014

ISBN 10 : 3639715756 ISBN 13 : 9783639715750

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. 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 Landstra�e 99, 66123 Saarbr�cken 164 pp. Englisch. N� de r�f. du vendeur 9783639715750

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