Large Scale Support Vector Machines Algorithms for Visual Recognition

Name: Large Scale Support Vector Machines Algorithms for Visual Recognition
Brand: Scholars' Press
SKU: 9783639715750
Price: 1571 CZK
Availability: InStock

Autor: Thanh-Nghi Doan, Francois Poulet

Jazyk:

Vazba: Brožovaná

Vydavatel: Scholars' Press

Dostupnost: Skladem u dodavatele

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Visual recognition remains an extremely challenging problem in computer vision. Most previous approa...

Informace o knize

Autor

Thanh-Nghi Doan, Francois Poulet

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2014

Stránek

164

EAN

9783639715750

ISBN

3639715756

Enbook ID

07172154

Vydavatel

Scholars' Press

Hmotnost

249

Rozměry

152 x 229 x 10

Kompletní popis

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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