Utilizing semantic word similarity measures for video retrieval

This is a high level computer vision paper, which employs concepts from Natural Language Understanding in solving the video retrieval problem. Our main contribution is the utilization of the semantic word similarity measures (Lin and PMI-IR similarities) for video retrieval. In our approach, we use trained concept detectors, and the visual co-occurrence relations between such concepts. We propose two methods for content-based retrieval of videos: (1) A method for retrieving a new concept(a concept which is not known to the system, and no annotation is available) using semantic word similarity and visual co-occurrence. (2) A method for retrieval of videos based on their relevance to a user defined text query using the semantic word similarity and visual content of videos. For evaluation purposes, we have mainly used the automatic search and the high level feature extraction test set of TRECVID'06 benchmark, and the automatic search test set of TRECVID'07. These two data sets ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Yusuf Aytar, Mubarak Shah, Jiebo Luo
• Keywords: Computer Vision, CVPR 2008, Semantic Retrieval Method, Semantic Word Similarity, Textbased Retrieval Method, Trained Concept Detectors, Video Retrieval Problem