Person Browser System Based on Named Entity Recognition for Broadcast News Interview Videos

Abstract

This work introduces a novel approach to extract meaningful content information from video by collaborative integration of image understanding and natural language processing. We developed a person browser system that associates faces and overlaid name texts in videos. This approach takes news videos as a knowledge source, then automatically extracts face and associated name text as content information. The proposed framework consists of the text detection module, the face detection module, and the person indexing database module. The successful results of person extraction reveal that the proposed methodology of integrated use of image understanding techniques and natural language processing technique is headed in the right direction to achieve our goal of accessing real content of multimedia information.