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Element rearrangement for action classification on product manifolds

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dc.contributor.authorKadappan, Karthik, author
dc.contributor.authorBeveridge, J. Ross, advisor
dc.contributor.authorMaciejewski, Anthony A., committee member
dc.contributor.authorPeterson, Chris, committee member
dc.contributor.authorRajopadhye, Sanjay, committee member
dc.date.accessioned2007-01-03T05:55:25Z
dc.date.available2007-01-03T05:55:25Z
dc.date.issued2013
dc.description.abstractConventional tensor-based classification algorithms unfold tensors into matrices using the standard mode-k unfoldings and perform classification using established machine learning algorithms. These methods assume that the standard mode-k unfolded matrices are the best 2-dimensional representations of N-dimensional structures. In this thesis, we ask the question: "Is there a better way to unfold a tensor?" To address this question, we design a method to create unfoldings of a tensor by rearranging elements in the original tensor and then applying the standard mode-k unfoldings. The rearrangement of elements in a tensor is formulated as a combinatorial optimization problem and tabu search is adapted in this work to solve it. We study this element rearrangement problem in the context of tensor-based action classification on product manifolds. We assess the proposed methods using a publicly available video data set, namely Cambridge-Gesture data set. We design several neighborhood structures and search strategies for tabu search and analyze their performance. Results reveal that the proposed element rearrangement algorithm developed in this thesis can be employed as a preprocessing step to increase classification accuracy in the context of action classification on product manifolds method.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierKadappan_colostate_0053N_11853.pdf
dc.identifier.urihttp://hdl.handle.net/10217/80250
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright 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.subjectaction classification
dc.subjectcomputer vision
dc.subjectelement rearrangement
dc.subjectmanifolds
dc.subjectTabu search
dc.subjecttensor
dc.titleElement rearrangement for action classification on product manifolds
dc.typeText
dcterms.rights.dplaThis 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.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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