Hierarchical Segmentation of Videos into Shots and Scenes using Visual Content - Thompson, Andrew (University of Ottawa (Canada), 2010)
With the large amounts of video data available, it has become increasingly important to have the ability to quickly search through and browse through these videos. With that in mind, the objective of this project is to facilitate the process of searching through videos for specific content by creating a video search tool, with an immediate goal of automatically performing a hierarchical segmentation of videos, particularly full-length movies, before carrying out a search for a specific query. We approach the problem by first segmenting the video into its film units. Once the units have been extracted, various similarity measures between features, that are extracted from the film units, can be used to locate specific sections in the movie. In order to be able to properly search through a film, we must first have access to its basic units. A movie can be broken down into a hierarchy of three units: frames, shots, and scenes. The important first step in this process is to partition the film into shots. Shot detection, the process of locating the transitions between different cameras, is executed by performing a color reduction, using the 4-Histograms method to calculate the distance between neighboring frames, applying a second order derivative to the resulting distance vector, and finally using the automatically calculated threshold to locate shot cuts. Scene detection is generally a more difficult task when compared to shot detection. After the shot boundaries of a video have been detected, the next step towards scene detection is to calculate a certain similarity measure which can then be used to cluster shots into scenes. Various keyframe extraction algorithms and similarity measures from the literature were considered and compared. Frame sampling for obtaining keyframe sets and Bhattacharya distance for similarity measure were selected for use in the shot detection algorithm. A binary shot similarity map is then created using the keyframe sets and Bhattacharya distance similarity measure. Next, a temporal distance weight and a predetermined threshold are applied to the map to obtain the final binary similarity map. The last step uses the proposed algorithm to locate the shot clusters along the diagonal which correspond to scenes. These methods and measures were successfully implemented in the Video Search Tool to hierarchically segment videos into shots and scenes.
Keywords: Engineering, Electronics and Electrical.