With the amount of video data increasing rapidly, automatic methods are needed to deal with large-scale video data sets in various applications. In content-based video analysis, a common and fundamental preprocess for these applications is video segmentation. Based on the segmentation results, video has a hierarchical representation structure of frames, shots, and scenes from the low level to high level. Due to the huge amount of video frames, it is not appropriate to represent video contents using frames. In the levels of video structure, shot is defined as an unbroken sequence of frames from one camera; however, the contents in shots are trivial and can hardly convey valuable semantic information. On the other hand, scene is a group of consecutive shots that focuses on an object or objects of interest. And a scene can represent a semantic unit for further processing such as story extraction, video summarization, etc. In this chapter, we will survey the methods on video scene segmentation. Specifically, there are two kinds of scenes. One kind of scene is to just consider the visual similarity of video shots and clustering methods are used for scene clustering. Another kind of scene is to consider both the visual similarity and temporal constraints of video shots, i.e., shots with similar contents and not lying too far in temporal order. Also, we will present our proposed methods on scene clustering and scene segmentation by using Gaussian mixture model, graph theory, sequential change detection, and spectral methods.
