Theses and Dissertations

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RIDMBC for object recognition using convolutional neural networks
(Colorado State University. Libraries, 2016) Agnihotri, Nikhil, author; Draper, Bruce A., advisor; Beveridge, Ross, advisor; Maciejewski, Anthony, committee member
Two 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.