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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...

The pixelization paradigm states as a postulate that pixelization methods are rich and are worth exploring as far as possible. In fact, we think that the strength of these methods lies in their simplicity, in their high-density way of information representation property and in their compatibility with neurocognitive processes. Simplicity, because pixelization belongs to two-dimensional information visualization methods and its main idea is identifying a pixel with an informational entity in order to translate a set of informational entities into an image. High-density way of information...