A Practical Video Fragment Identification System

Abstract

In this paper, we present a practical identification approach of video fragment for digital video files. Before analyzing the video content, we must decode it based on its encoding format first. In order to effectively identify the format of a fragment, a format classification is performed before the format identification. The methods of format classification and identification are discriminative subspace clustering (DiSC) and the Knearest neighbor (KNN).Because of losing the meta-information, we add a maximum similar header (MSH) to the front of the fragment to recover the video content. We adopt a simple key frame detection method using standard deviation and mean value. Motion vectors of macro blocks are utilized to classify the video features for effectively identifying the video. Several edges of frames are accumulated and compose a video feature. The experimental results show the evaluations of the video format classification and identification, fragment recovery, and content identification.