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Semantic context detection is one of the key techniques to facilitate efficient multimedia retrieval. Semantic context is a scene that completely represents a meaningful information segment to human beings. In this paper, we propose a novel hierarchical approach that models the statistical characteristics of several audio events, over a time series, to(More)
—With the idea of social network analysis, we propose a novel way to analyze movie videos from the perspective of social relationships rather than audiovisual features. To appropriately describe role's relationships in movies, we devise a method to quantify relations and construct role's social networks, called RoleNet. Based on RoleNet, we are able to(More)
With the advances of digital video analysis and storage technologies, also the progress of entertainment industry, movie viewers hope to gain more control over what they see. Therefore, tools that enable movie content analysis are important for accessing, retrieving, and browsing information close to a human perceptive and semantic level. We proposed an(More)
We present a brave new way to analyze movie content, from the perspectives of the relationships between roles rather than low-level audiovisual features. Interactions between roles in a movie resemble human behaviors in a society. Roles' actions lead the story and make viewers understand what directors want to present. In this paper, we introduce the idea(More)
—A framework for scrutinizing baseball videos is proposed. By applying the well-defined baseball rules, this work exactly identifies what happens in a game rather than roughly finding some interesting parts. After extracting the information changes on the superimposed caption, a rule-based decision is applied to detect meaningful events. Only three types of(More)
Near-duplicate detection techniques are exploited to facilitate representative photo selection and region-of-interest (ROI) determination, which are important functionalities for efficient photo management and browsing. To make near-duplicate detection module resist to noisy features, three filtering approaches, i.e., point-based, region-based, and(More)
We introduce local feature points to achieve face clustering for consumer photos. After combining eigenfaces with context information like clothes, we further investigate the usage of local feature points to match face images. The relationships between face images are constructed by feature matching and then described as a graph. Outliers in the results of(More)
Semantic-level content analysis is a crucial issue to achieve efficient content retrieval and management. We propose a hierarchical approach that models the statistical characteristics of several audio events over a time series to accomplish semantic context detection. Two stages, including audio event and semantic context modeling/testing, are devised to(More)