A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
Scene change detection techniques for video database systems
Multimedia Systems
Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
A feature-based algorithm for detecting and classifying production effects
Multimedia Systems
On the detection and recognition of television commercials
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Morphological Tools for Indexing Video Documents
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
Bridging the semantic gap in sports video retrieval and summarization
Journal of Visual Communication and Image Representation
A hierarchical framework for generic sports video classification
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Event based indexing of broadcasted sports video by intermodalcollaboration
IEEE Transactions on Multimedia
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Automatic summarization of cricket video events using genetic algorithm
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Bayesian belief network based broadcast sports video indexing
Multimedia Tools and Applications
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In this paper we address the problem of temporal segmentation of videos. We present a multi-modal approach where clues from different information sources are merged to perform the segmentation. Specifically, we segment videos based on textual descriptions or commentaries of the action in the video. Such a parallel information is available for cricket videos, a class of videos where visual feature based (bottom-up) scene segmentation algorithms generally fail, due to lack of visual dissimilarity across space and time. With additional top-down information from textual domain, these ambiguities could be resolved to a large extent. The video is segmented to meaningful entities or scenes, using the scene level descriptions provided by the commentary. These segments can then be automatically annotated with the respective descriptions. This allows for a semantic access and retrieval of video segments, which is difficult to obtain from existing visual feature based approaches. We also present techniques for automatic highlight generation using our scheme.