"Tampa" . "Haindl, Michal" . "Los Alamitos" . . . . "Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences" . . . . "Filip, Ji\u0159\u00ED" . "4"^^ . "Proceedings of the 19th International Conference on Pattern Recognition" . . "video segmentation"@en . . "RIV/67985556:_____/08:00317590!RIV10-AV0-67985556" . . "P(1ET400750407), P(GA102/08/0593), Z(AV0Z10750506)" . . . . . . . "Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences" . "IEEE Press" . . "With significantly increasing number of archived movie sequences a need of their automatic indexation and annotation is raising. Robust and fast temporal segmentation of video sequences is one of the challenging research topics in this area. In this paper we propose a new temporal segmentation method of the video sequences based on PCA approach. Contrary to standard approaches based on histogram or motion field analysis the proposed method does not require any such a complex analysis. The method starts with sparse greyscale sampling and eigen-analysis of input sequence. A sum of absolute derivatives of temporal mixing coefficients of main eigen-images is then used as cuts detection feature, while dissolve transitions are detected by means of coefficients' specific behaviour. The functionality of the method was successfully tested on number of sequences ranging from artificial set of similar dynamic textures to professional documentary movies." . . "[B84156AE45C1]" . "978-1-4244-2174-9" . "Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences"@en . "2"^^ . "Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences"@en . "RIV/67985556:_____/08:00317590" . "367683" . "With significantly increasing number of archived movie sequences a need of their automatic indexation and annotation is raising. Robust and fast temporal segmentation of video sequences is one of the challenging research topics in this area. In this paper we propose a new temporal segmentation method of the video sequences based on PCA approach. Contrary to standard approaches based on histogram or motion field analysis the proposed method does not require any such a complex analysis. The method starts with sparse greyscale sampling and eigen-analysis of input sequence. A sum of absolute derivatives of temporal mixing coefficients of main eigen-images is then used as cuts detection feature, while dissolve transitions are detected by means of coefficients' specific behaviour. The functionality of the method was successfully tested on number of sequences ranging from artificial set of similar dynamic textures to professional documentary movies."@en . "2008-12-07+01:00"^^ . . "2"^^ .