Sparse representations in multi-kernel dictionaries for in-situ classification of underwater objects
| dc.contributor.author | Hosseini, Somayeh, author | |
| dc.contributor.author | Pezeshki, Ali, advisor | |
| dc.contributor.author | Azimi-Sadjadi, Mahmood R., advisor | |
| dc.contributor.author | Chong, Edwin, committee member | |
| dc.contributor.author | Luo, Jie, committee member | |
| dc.contributor.author | Kirby, Michael, committee member | |
| dc.date.accessioned | 2017-06-09T15:41:06Z | |
| dc.date.available | 2017-06-09T15:41:06Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | The performance of the kernel-based pattern classification algorithms depends highly on the selection of the kernel function and its parameters. Consequently in the recent years there has been a growing interest in machine learning algorithms to select kernel functions automatically from a predefined dictionary of kernels. In this work we develop a general mathematical framework for multi-kernel classification that makes use of sparse representation theory for automatically selecting the kernel functions and their parameters that best represent a set of training samples. We construct a dictionary of different kernel functions with different parametrizations. Using a sparse approximation algorithm, we represent the ideal score of each training sample as a sparse linear combination of the kernel functions in the dictionary evaluated at all training samples. Moreover, we incorporate the high-level operator's concepts into the learning by using the in-situ learning for the new unseen samples whose scores can not be represented suitably using the previously selected representative samples. Finally, we evaluate the viability of this method for in-situ classification of a database of underwater object images. Results are presented in terms of ROC curve, confusion matrix and correct classification rate measures. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Hosseini_colostate_0053N_14058.pdf | |
| dc.identifier.uri | http://hdl.handle.net/10217/181340 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.title | Sparse representations in multi-kernel dictionaries for in-situ classification of underwater objects | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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