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Interactive Dynamic Influence Diagrams (I-DIDs) are a general decision making framework for multiple agents that are either collaborative or competitive. The framework allows for agents to plan individually at their own level in the context of other agents acting and observing in a partially observable environment. Most of the I-DID techniques focus on a simple setting of two agents in which one subject...
Mining high dimensional data-sets extracted from real world problems is a challenging task due to the large features' space. The latent variables are used to reduce the dimensions of this space by representing highly dependent features. They simplify the creation of probabilistic models and they clarify the semantic of the inferred knowledge. Learning these variables for Bayesian network, as the most...
Latent Dirichlet allocation (LDA) is a generative probabilistic model of discrete data, where each observed item is represented as a finite mixture over latent topics. Several multi-modal extensions of LDA to model annotated data are available for image annotation. Most of existing methods model the joint distribution of image features and caption texts, in order to capture statistical correlations...
We propose a novel method of analyzing human interactions based on the walking trajectories of human subjects, which provide elementary and necessary components for understanding and interpretation of complex human interactions in visual surveillance tasks. Our principal assumption is that an interaction episode is composed of meaningful small unit interactions, which we call “sub-interactions.” We...
In this paper, we propose a novel method for analyzing human interactions based on the walking trajectories of human subjects. Our principal assumption is that an interaction episode is composed of meaningful smaller unit interactions, which we call `sub-interactions.' The whole interaction is represented by an ordered concatenation or a network of sub-interaction models. From the experiments, we...
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