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Towards Learning Representations in Visual Computing Tasks


Abstract The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos.

The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-cra... (more)
Created Date 2017
Contributor Chandakkar, Parag Shridhar (Author) / Li, Baoxin (Advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Subject Artificial intelligence / Computer science / Deep Learning / Feature Engineering / Learning Representations / Machine Learning / Visual Computing
Type Doctoral Dissertation
Extent 183 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis