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Learning with Structured Data: Applications to Computer Vision

eBook (PDF), 192 Pages
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This thesis addresses structured machine learning problems. Here "structured" refers to situations in which the input or output domain of a prediction function is non-vectorial. Instead, the input instance or the predicted value can be decomposed into parts that follow certain dependencies, relations and constraints. The first part of the thesis considers structure in the input domain and a general framework based on the notion of substructures is developed. The framework is broadly applicable and two computer vision problems illustrate this: class-level object recognition and human action recognition. The second part of the thesis addresses structure in the output domain of a prediction function. Specifically image segmentation problems in which the produced segmentation must satisfy global properties such as connectivity are analyzed. As a result, a principled method to incorporate global interactions into computer vision random field models by means of linear... More > programming relaxations is given.< Less
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Product Details

Published
September 28, 2011
Language
English
Pages
192
File Format
PDF
File Size
13.37 MB

Formats for this Ebook

PDF
Required Software Any PDF Reader, Apple Preview
Supported Devices Windows PC/PocketPC, Mac OS, Linux OS, Apple iPhone/iPod Touch... (See More)
# of Devices Unlimited
Flowing Text / Pages Pages
Printable? Yes
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