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dc.contributor.advisorDavis, Larry Sen_US
dc.contributor.authorOzdemir, Bahadiren_US
dc.date.accessioned2016-09-03T05:31:50Z
dc.date.available2016-09-03T05:31:50Z
dc.date.issued2016en_US
dc.identifierdoi:10.13016/M2C214
dc.identifier.urihttp://hdl.handle.net/1903/18533
dc.description.abstractThe size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.en_US
dc.language.isoenen_US
dc.titleLearning Binary Code Representations for Effective and Efficient Image Retrievalen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledBinary Codesen_US
dc.subject.pquncontrolledGaussian Processen_US
dc.subject.pquncontrolledHashingen_US
dc.subject.pquncontrolledImage Retrievalen_US
dc.subject.pquncontrolledIndian Buffet Processen_US
dc.subject.pquncontrolledOnline Learningen_US


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