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Regular Paper Issue
EmotionMap: Visual Analysis of Video Emotional Content on a Map
Journal of Computer Science and Technology 2020, 35 (3): 576-591
Published: 29 May 2020
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Emotion plays a crucial role in gratifying users’ needs during their experience of movies and TV series, and may be underutilized as a framework for exploring video content and analysis. In this paper, we present EmotionMap, a novel way of presenting emotion for daily users in 2D geography, fusing spatio-temporal information with emotional data. The interface is composed of novel visualization elements interconnected to facilitate video content exploration, understanding, and searching. EmotionMap allows understanding of the overall emotion at a glance while also giving a rapid understanding of the details. Firstly, we develop EmotionDisc which is an effective tool for collecting audiences’ emotion based on emotion representation models. We collect audience and character emotional data, and then integrate the metaphor of a map to visualize video content and emotion in a hierarchical structure. EmotionMap combines sketch interaction, providing a natural approach for users’ active exploration. The novelty and the effectiveness of EmotionMap have been demonstrated by the user study and experts’ feedback.

Open Access Research Article Issue
VideoMap: An interactive and scalable visualization for exploring video content
Computational Visual Media 2016, 2 (3): 291-304
Published: 06 May 2016
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Large-scale dynamic relational data visualization has attracted considerable research attention recently. We introduce dynamic data visualization into the multimedia domain, and present an interactive and scalable system, VideoMap, for exploring large-scale video content. A long video or movie has much content; the associations between the content are complicated. VideoMap uses new visual representations to extract meaningful information from video content. Map-based visualization naturally and easily summarizes and reveals important features and events in video. Multi-scale descriptions are used to describe the layout and distribution of temporal information, spatial information, and associations between video content. Firstly, semantic associations are used in which map elements correspond to video contents. Secondly, video contents are visualized hierarchically from a large scale to a fine-detailed scale. VideoMap uses a small set of sketch gestures to invoke analysis, and automatically completes charts by synthesizing visual representations from the map and binding them to the underlying data. Furthermore, VideoMap allows users to use gestures to move and resize the view, as when using a map, facilitating interactive exploration. Our experimental evaluation of VideoMap demonstrates how the system can assist in exploring video content as well as significantly reducing browsing time when trying to understand and find events of interest.

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