Automatic partitioning of full-motion video
Multimedia Systems
A framework for video scene boundary detection
Proceedings of the tenth ACM international conference on Multimedia
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Video Shot Detection Using Hidden Markov Models with Complementary Features
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Probabilistic shot boundary detection using interframe histogram differences
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Shot boundary detection algorithm in compressed domain based on adaboost and fuzzy theory
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Shot boundary detection based on SVM and TMRA
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
Towards Theoretical Performance Limits of Video Parsing
IEEE Transactions on Circuits and Systems for Video Technology
Dictionary-based color image retrieval using multiset theory
Journal of Visual Communication and Image Representation
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We have proposed a efficient and robust shot segmentation algorithm that is based on a well known Lempel-Ziv-Welch (LZW) text compression technique. The algorithm works on the fact that, when a frame is compressed using its previous/reference frame information, the 'compression ratio' obtained gives the direct measure of similarity between them. The novelty of the algorithm lies in, i) exploiting the characteristics of LZW technique in capturing the primary image features such as color and texture information, and representing them as a single feature called patterns; ii) easy handling of inter shot video effects such as abrupt, dissolves, fades, wipes, and intra shot video effects such as object entry/exit, slow/fast motion; and iii) efficient identification of shot boundaries and low computation complexity of the algorithm. The performance of the proposed algorithm is tested with a wide varieties of videos from a set of standard databases and with our lab video collection. The experimentation results promises a high accuracy and robustness in identifying the shot boundaries.