MPEG Video Compression Standard
MPEG Video Compression Standard
A fast algorithm for video parsing using MPEG compressed sequences
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
A Unified Approach to Temporal Segmentation of Motion JPEG and MPEG Compressed Video
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Manipulation and compositing of MC-DCT compressed video
IEEE Journal on Selected Areas in Communications
Shot Change Detection Using Scene-Based Constraint
Multimedia Tools and Applications
Hybrid Rule-Based/Neural Approach for Segmentation of MPEG Compressed Video
Multimedia Tools and Applications
Shot Partitioning Based Recognition of TV Commercials
Multimedia Tools and Applications
A Region Tracking Method with Failure Detection for an Interactive Video Indexing Environment
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
VADIS: A Video Analysis, Display and Indexing System
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Detecting camera movements & production effects in digital videos
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Learning to segment a video to clips based on scene and camera motion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
| Hi-index | 0.00 |
Temporal segmentation of video is a necessary first step to indexing digital video for browsing and retrieval. A number of different video temporal segmentation algorithms have been published in the literature. There has been little effort to evaluate and characterize their performance so as to deliver a single (or set of) algorithms that may be used by other researchers for indexing video databases. We present results of evaluating a number of these algorithms and characterizing their performance, specifically with respect to robustness to encoder and bitrate changes. The lessons learnt have relevance to algorithm development and evaluation in general.