Geometry-Aware Analysis of High-Dimensional Visual Information Sets
published: Feb. 1, 2011, recorded: November 2010, views: 1163
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !