Content-Based Video Segment Reunification for TV Program Extraction

Abstract

This paper addresses the problem of automatic broadcasted TV program extraction from the low-level video data without using any metadata. In this context, the TV stream is first segmented. Segments are then classified into two categories: segments of inter-programs (e.g. commercials) and segments of programs that are parts of broadcasted TV programs (e.g. films, news, shows). One TV program can hence be split into several parts over a set of consecutive program segments. Consecutive program segments of the same TV program thus have to be reunified or fused in order to retrieve the entire TV program. This consecutive program segment reunification is the main concern of the paper. We focus in particular on the case where no metadata is available. We assume that the different parts of a same TV program share a set of features. Hence, our solution relies on analyzing the visual content and characteristics of each pair of consecutive segments in order to decide if they have to be reunified or not. It uses, amongst others, content-based descriptors like the color distribution, the number of faces in each segment and also the number of near-identical shots between the two segments. These descriptors are then used within an SVM classifier which makes the final decision. The effectiveness of the solution has been shown experimentally using a real TV stream of three weeks.