Video Classification and Retrieval Using Arabic Closed Caption

Vast volumes of digital video data are generated recently in our daily life. One of the most challenging problems is classifying and retrieving the desired information from huge collections of digital video. Consequently, the closed caption text has been utilized as an alternative to enhance the video retrieval and classification. Some systems are designed based on English closed caption however results have shown that Arabic is not lucky as English and other European languages in the research. This paper adopts an approach that enables video scenes classification and retrieving based on the Arabic closed-caption text that is present in the video. Experiments are performed over prepared dataset collected from Arabic news videos and Arabic documentary films across different Arabic channels. The results show that the proposed framework is efficient for retrieving Arabic videos and also for classifying Arabic video scenes into a set of eight predefined semantic categories including politics, economics, sports, religion, social, tourism, weather, and health.