Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Application of computational media aesthetics methodology to extracting color semantics in film
Proceedings of the tenth ACM international conference on Multimedia
Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
Automated location matching in movies
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Extraction of Film Takes for Cinematic Analysis
Multimedia Tools and Applications
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding structure in home videos by probabilistic hierarchical clustering
IEEE Transactions on Circuits and Systems for Video Technology
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The identification of useful structures in home video is difficult because this class of video is distinguished from other video sources by its unrestricted, non edited content and the absence of regulated storyline. In addition, home videos contain a lot of motion and erratic camera movements, with shots of the same character being captured from various angles and viewpoints. In this paper, we present a solution to the challenging problem of clustering shots and faces in home videos, based on the use of SIFT features. SIFT features have been known to be robust for object recognition; however, in dealing with the complexities of home video setting, the matching process needs to be augmented and adapted. This paper describes various techniques that can improve the number of matches returned as well as the correctness of matches. For example, existing methods for verification of matches are inadequate for cases when a small number of matches are returned, a common situation in home videos. We address this by constructing a robust classifier that works on matching sets instead of individual matches, allowing the exploitation of the geometric constraints between matches. Finally, we propose techniques for robustly extracting target clusters from individual feature matches.