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Developing effective machine learning methods for multimedia data modeling continues to challenge computer vision scientists. The capability of providing effective learning models can have significant impact on various applications. In this work, we propose a nonparametric Bayesian approach to address simultaneously two fundamental problems, namely clustering and feature selection. The approach is...
Exploiting Dirichlet process mixture models (also known as infinite mixture models) to model visual and textual data is now standard weapon in the arsenal of machine learning. This paper proposes a new accelerated variational inference approach to learn Dirichlet process mixture models with Dirichlet distributions. The choice of using Dirichlet distribution as the basic distribution is mainly due...
Human activity recognition is a crucial area of computer vision research and applications. The goal of human activity recognition aims to automatically analyze and interpret ongoing events and their context from video data. Recently, the bag of visual words (BoVW) approach has been widely applied for human action recognition. Generally, a representative corpus of videos is used to build the Visual...
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