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Geometry-aware analysis of high-dimensional visual information sets

Geometry-aware analysis of high-dimensional visual information sets Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobile devices and vision sensors have led to the emergence of novel multimedia mining architectures. These produce even more multimedia data, which are possibly captured under geometric transformations and need to be efficiently stored and analyzed. It is also common in such systems that data are collected distributively. This very fact poses great challenges in the design of effective methods for analysis and knowledge discovery from multimedia data. In this thesis, we study various instances of the problem of classification of visual data under the view-point of modern challenges. Roughly speaking, classification corresponds to the problem of categorizing an observed object to a particular class (or category), based on previously seen examples. We address important issues related to classification, namely flexible data representation for joint coding and classification, robust classification in the case of large geometric transformations and classification with multiple object observations in both centralized and distributed settings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGMultimedia Records Association for Computing Machinery

Geometry-aware analysis of high-dimensional visual information sets

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References (2)

Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Classifier design and evaluation
ISSN
1947-4598
DOI
10.1145/1660921.1660924
Publisher site
See Article on Publisher Site

Abstract

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobile devices and vision sensors have led to the emergence of novel multimedia mining architectures. These produce even more multimedia data, which are possibly captured under geometric transformations and need to be efficiently stored and analyzed. It is also common in such systems that data are collected distributively. This very fact poses great challenges in the design of effective methods for analysis and knowledge discovery from multimedia data. In this thesis, we study various instances of the problem of classification of visual data under the view-point of modern challenges. Roughly speaking, classification corresponds to the problem of categorizing an observed object to a particular class (or category), based on previously seen examples. We address important issues related to classification, namely flexible data representation for joint coding and classification, robust classification in the case of large geometric transformations and classification with multiple object observations in both centralized and distributed settings.

Journal

ACM SIGMultimedia Records – Association for Computing Machinery

Published: Mar 1, 2009

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