A Semantic Image Category for Structuring TV Broadcast Video Streams

Abstract

TV broadcast video stream consists of various kinds of programs such as sitcoms, news, sports, commercials, weather, etc. In this paper, we propose a semantic image category, named as Program Oriented Informative Images (POIM), to facilitate the segmentation, indexing and retrieval of different programs. The assumption is that most stations tend to insert lead-in/-out video shots for explicitly introducing the current program and indicating the transitions between consecutive programs within TV streams. Such shots often utilize the overlapping of text, graphics, and storytelling images to create an image sequence of POIM as a visual representation for the current program. With the advance of post-editing effects, POIM is becoming an effective indicator to structure TV streams, and also is a fairly common “prop” in program content production. We have attempted to develop a POIM recognizer involving a set of global/local visual features and supervised/unsupervised learning. Comparison experiments have been carried out. A promising result, F1 = 90.2%, has been achieved on a part of TRECVID 2005 video corpus. The recognition of POIM, together with other audiovisual features, can be used to further determine program boundaries.