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Bayesian Prediction and Regression from Visual Data : 영상 데이터에 대한 베이지안 예측 및 회귀 분석
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- Authors
- Advisor
- 최진영
- Major
- 공과대학 전기·컴퓨터공학부
- Issue Date
- 2017-02
- Publisher
- 서울대학교 대학원
- Keywords
- 회귀분석 ; 확률적 그래프 모델 ; 계층적 생성 모델 ; 근사 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
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