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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