Developing context model supporting spatial relations for semantic video retrieval

Video retrieval is one of the most famous issues in multimedia research. Users express their needs in terms of queries and expect to retrieve most relevant answers. This task is becoming harder due to large amount of video archives including broadcast news, documentary videos, movies and particularly internet which let users to search and share videos all over the world. Retrieving videos is challenging since queries can be on object, motion, texture, color, audio, etc. Indexing the video content supporting spatial relations will give more accurate results. For this purpose, segmentation and localization should be done. In this work, we propose to find new concept without availability of annotation and then retrieve videos according to queries defined by users and support spatial relations. To do mentioned purposes, we developed context model supporting spatial relations for better retrieval results. Our main contribution for better video retrieval is extracting spatial information in addition to using semantic word similarity for finding new concept without availability of annotations.