Enhancing Video Viewing Experience

Enhancing Video Viewing Experience

Akio Takashima (Tokyo University of Technology, Japan)
DOI: 10.4018/978-1-61520-851-7.ch006
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Abstract

In this study, the authors have considered video viewing style to be a type of knowledge medium. Video viewing styles, which are considered to be habitual behaviors during video viewing, are used to externalize one’s viewing skills or know-how about video viewing; they allow users to experience videos through these skills. In order to allow users to experience videos in various viewing styles, the authors have developed a system called the video viewing experience reproducer (VVER), which determines the user’s viewing styles and reuses them. To determine these styles, the system extracts associations between the user’s manipulation of videos and the low-level features of these videos. In this chapter, the authors describe the notion of reusing the video viewing styles and composing them. After discussing examples of utilizing this concept, preliminary user studies have been reported.
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Assisting People In Video Viewing

To support the video viewing process, numerous studies focusing on content-based analyses for retrieving or summarizing videos have been reported (Nakamura & Kanade, 1997; Ekin et al., 2003). These studies assume that content-based knowledge may include the semantic information of video data; in other words, it includes generally accepted domain knowledge. For example, people tend to pay additional attention to goal scenes during football games or captions during news programs that describe the summary or location of the news topic. Therefore, these approaches are applicable only for specific purposes (e.g., extracting goal scenes from football games as important scenes), which are assumed beforehand. However, it is difficult to determine the scenes of interest for a particular user. Some users might like to watch a summarized video, which focuses particularly on the goal scenes in a football game video, whereas other viewers might want to watch the scenes in which a particular star player appears. Further, in the case of a weather forecast video, users might want to watch the scenes showing the area in which they reside (Figure 1). Therefore, it is difficult for systems using content-based knowledge to satisfy the needs of users because the users’ intention or knowledge, which determines what they want to watch, is not entirely predictable, and occasionally, the users themselves cannot distinctly describe their viewing requirements.

Figure 1.

It is difficult for systems using content-based knowledge to satisfy the needs of users because the users’ intention or knowledge, which determines what they want to watch, is not entirely predictable, and occasionally, the users themselves cannot distinctly describe their viewing requirements

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