Distributional clustering for content-based browsing of unstructured video

Abstract

The focus of this paper is to facilitate access into an important class of multimedia content: unscripted and causally shot home videos. Unlike scripted video (such as movies and commercials), video that has been shot for the simple purpose of recording an event or place has no structure to facilitate its access. Our approach to tackling the unstructured video problem is to use clustering techniques to create multiple groupings of the video. We describe two clustering algorithms in this paper, which assume fairly general probability density descriptions of video. In addition, we describe an application built upon these clustering algorithms that enables browsing and rescripting unstructred content.