Automatic textual annotation of video news based on semantic visual object extraction

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

• Type: Conference
• Year: 2004
• Where: SPIESR
• Authors: Nozha Boujemaa, François Fleuret, Valérie Gouet, Hichem Sahbi
• Keywords: Database, Semantic Object Detection, Semantic Objects, SPIESR 2004, Tv Logos