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Bayesian Prediction and Regression from Visual Data
영상 데이터에 대한 베이지안 예측 및 회귀 분석

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Authors
유영준
Advisor
최진영
Major
공과대학 전기·컴퓨터공학부
Issue Date
2017-02
Publisher
서울대학교 대학원
Keywords
회귀분석확률적 그래프 모델계층적 생성 모델근사 추론
Description
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 최진영.
Abstract
This dissertation proposes a new high dimensional regression / prediction method for
diverse visual data pairs. In contrast to other regression / prediction methods, the proposed
method focuses on the case where output responses are on a complex high dimensional
manifold, such as images. In handling the complex data, the latent space
embedding the information of the data is used for efficient regression / prediction. The
dimensionality reduction methods into the latent space and the regression/prediction
methods are designed as a Bayesian framework. For the prediction problem, the dissertation
proposes a method to extract latent semantics on motion dynamics given in
visual sequences. To this end, a Bayesian inference model is developed to capture
the regional and temporal semantics of the dynamics data. The proposed Bayesian
model is a hierarchical fusion of Gaussian mixture model and topic mixture model. It
finds regional pattern information through topic mixture model and derives temporal
co-occurrence of regional patterns through Gaussian mixture model. To infer the proposed
model, the dissertation proposes a new sampling method that enables efficient
inference. For the regression problem, we propose a method that makes a regression
in the latent space for general and complex visual data pairs. This allows the latent space to imply the essential properties of the data pairs required for regression. For the purpose, a regression model is designed so that the regression in latent space should coincide with the regression in data space. The whole models are designed as Bayesian framework, and inferred by variational autoencoder framework.
Language
English
URI
https://hdl.handle.net/10371/119274
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Theses (Ph.D. / Sc.D._전기·정보공학부)
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