A text-based fully automated architecture for the semantic annotation and retrieval of Turkish news videos

Video texts are known to constitute an important source of information for semantic summaries of video archives. In this study, we propose a fully automated architecture for semantic annotation and later retrieval of Turkish news videos based on the corresponding video texts. At the core of the architecture is a named entity recognizer, the output of which on video texts is used as semantic annotations for the corresponding videos. The architecture also comprises components for news story segmentation, sliding text recognition, and video retrieval in addition to a news video database. The news story segmentation module makes use of the audio waveforms of the raw video files to detect the boundaries of individual news stories. The sliding text recognizer is then executed on the video segments corresponding to these news stories to extract their texts. The texts are then fed into the named entity recognizer for Turkish news texts to extract the named entities which are to be used as semantic annotations or index terms for the retrieval of these news videos. Finally, the retrieval interface of the overall architecture enables access to the annotated videos and video segments through boolean queries formed by using the previously extracted named entities. This study is significant for its proposing the first fully automated architecture for the semantic annotation and retrieval of Turkish news video archives.