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Nonparametric Bayesian Models for Joint Analysis of Imagery and Text

dc.contributor.advisor Carin, Lawrence Li, Lingbo 2014-05-14T19:16:59Z 2014-05-14T19:16:59Z 2014
dc.description.abstract <p>It has been increasingly important to develop statistical models to manage large-scale high-dimensional image data. This thesis presents novel hierarchical nonparametric Bayesian models for joint analysis of imagery and text. This thesis consists two main parts.</p><p>The first part is based on single image processing. We first present a spatially dependent model for simultaneous image segmentation and interpretation. Given a corrupted image, by imposing spatial inter-relationships within imagery, the model not only improves reconstruction performance but also yields smooth segmentation. Then we develop online variational Bayesian algorithm for dictionary learning to process large-scale datasets, based on online stochastic optimization with a natu- ral gradient step. We show that dictionary is learned simultaneously with image reconstruction on large natural images containing tens of millions of pixels.</p><p>The second part applies dictionary learning for joint analysis of multiple image and text to infer relationship among images. We show that feature extraction and image organization with annotation (when available) can be integrated by unifying dictionary learning and hierarchical topic modeling. We present image organization in both "flat" and hierarchical constructions. Compared with traditional algorithms feature extraction is separated from model learning, our algorithms not only better fits the datasets, but also provides richer and more interpretable structures of image</p>
dc.subject Electrical engineering
dc.subject Statistics
dc.subject Computer science
dc.subject Bayesian Nonparametrics
dc.subject Dictionary Learning
dc.subject Image Processing
dc.subject Machine Learning
dc.subject Topic Modeling
dc.title Nonparametric Bayesian Models for Joint Analysis of Imagery and Text
dc.type Dissertation
dc.department Electrical and Computer Engineering

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