Automatic text processing
Automatic content-based retrieval of broadcast news
Proceedings of the third ACM international conference on Multimedia
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
Story Segmentation and Detection of Commercials in Broadcast News Video
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Key-frame extraction algorithm using entropy difference
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Semantics reinforcement and fusion learning for multimedia streams
Proceedings of the 6th ACM international conference on Image and video retrieval
Information-theoretic semantic multimedia indexing
Proceedings of the 6th ACM international conference on Image and video retrieval
Investigating keyframe selection methods in the novel domain of passively captured visual lifelogs
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Fast video retrieval under sparse training data
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
ANSES: summarisation of news video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
MultiFusion: A boosting approach for multimedia fusion
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A design-of-experiment based statistical technique for detection of key-frames
Multimedia Tools and Applications
| Hi-index | 0.00 |
We evaluate the application of feature-vector based image retrieval methods to the problem of video retrieval. A vast number of primitive features is calculated for each of the key frames generated by a segmentation process, and we examine the use of three methods for retrieving video segments using the features -- a vector space model, a learning method using the AdaBoost algorithm, and a k-nearest neigh-bour approach.