Content based Video Retrieval: A Survey

Abstract

are a powerful and communicative media that can capture and present information. The rapidly expanding digital video information has motivated growth of new technologies for effective browsing, annotating and retrieval of video data. Content-based video retrieval has attracted wide research during the last 10 years. Users are more diverted to content based search rather than text based search. These lead to the process of selecting, indexing and ranking the database according to the human visual perception. This paper reviews the recent research in content based video retrieval system. This survey focusing on video structure analysis, like, shot boundary detection and key frame extraction, different feature extraction methods including SIFT, SURF, etc, similarity measure, video indexing, and video browsing. This system retrieves similar videos based on local feature descriptor called SURF (Speeded-Up Robust Feature). For image convolution SURF relies on integral images. In SURF we use Hessian matrix-based measure for the detector and a distribution-based descriptor. SURF can be computed and compared much faster with respect to repeatability, uniqueness and robustness. SURF is better than previous proposed methods as SIFT, PCA-SIFT, GLOH, etc. Finally the future scope in this system is specified.