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Sparse Methods in Image Understanding and Computer Vision

Abstract Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire... (more)
Created Date 2013
Contributor Jayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Advisor) / Frakes, David (Committee member) / Tepedelenlioglu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Computer science / Computer Vision / Dictionary Learning / Image Understanding / Machine Learning / Optimization / Sparse Representations
Type Doctoral Dissertation
Extent 268 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Electrical Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis