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RIDMBC for object recognition using convolutional neural networks

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dc.contributor.authorAgnihotri, Nikhil, author
dc.contributor.authorDraper, Bruce A., advisor
dc.contributor.authorBeveridge, Ross, advisor
dc.contributor.authorMaciejewski, Anthony, committee member
dc.date.accessioned2016-07-13T14:50:30Z
dc.date.available2016-07-13T14:50:30Z
dc.date.issued2016
dc.description.abstractTwo trending techniques that are making advances in computer vision research are Convolutional Neural Networks and Visual Hashing. The goal of this paper is to analyze how these two interact in the broad domain of objects. Deep neural nets have proved to broadly represent image features, and binary codes have proved to be a powerful way to represent the intrinsic nature of image content in a compact way. Our research explores what kind of information is contained in feature vectors obtained from deep neural nets and what infor- mation can be binarized, in the context of object recognition. We also try to optimize the length of binary codes and select subsets of bit vectors to represent images so as to obtain the best classification results, while trying to bring down computational cost.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierAGNIHOTRI_colostate_0053N_13612.pdf
dc.identifier.urihttp://hdl.handle.net/10217/173560
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.subjectcomputer vision
dc.subjectobject recognition
dc.subjectneural networks
dc.subjectbinary codes
dc.titleRIDMBC for object recognition using convolutional neural networks
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.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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