In recent years, more and more people capture their experiences in home videos. However, home video editing still is a
difficult and time-consuming task. We present the Edit While Watching system that allows users to automatically create
and change a summary of a home video in an easy, intuitive and lean-back way. Based on content analysis, video is
indexed, segmented, and combined with proper music and editing effects. The result is an automatically generated home
video summary that is shown to the user. While watching it, users can indicate whether they like certain content, so that
the system will adapt the summary to contain more content that is similar or related to the displayed content. During the
video playback users can also modify and enrich the content, seeing immediately the effects of their changes. Edit While
Watching does not require a complex user interface: a TV and a few keys of a remote control are sufficient. A user study
has shown that it is easy to learn and to use, even if users expressed the need for more control in the editing operations
and in the editing process.
The ability to summarize and abstract information will be an essential part of intelligent behavior in consumer devices. Various summarization methods have been the topic of intensive research in the content-based video analysis community. Summarization in traditional information retrieval is a well understood problem. While there has been a lot of research in the multimedia community there is no agreed upon terminology and classification of the problems in this domain. Although the problem has been researched from different aspects there is usually no distinction between the various dimensions of summarization. The goal of the paper is to provide the basic definitions of widely used terms such as skimming, summarization, and highlighting. The different levels of summarization: local, global, and meta-level are made explicit. We distinguish among the dimensions of task, content, and method and provide an extensive classification model for the same. We map the existing summary extraction approaches in the literature into this model and we classify the aspects of proposed systems in the literature. In addition, we outline the evaluation methods and provide a brief survey. Finally we propose future research directions based on the white spots that we identified by analysis of existing systems in the literature.