Parametric model for video content analysis
Pattern Recognition Letters
Monte Carlo Based Algorithm for Fast Preliminary Video Analysis
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Video scene segmentation and semantic representation using a novel scheme
Multimedia Tools and Applications
Movie segmentation into scenes and chapters using locally weighted bag of visual words
Proceedings of the ACM International Conference on Image and Video Retrieval
Scene detection in videos using shot clustering and sequence alignment
IEEE Transactions on Multimedia
Graph-based multilevel temporal segmentation of scripted content videos
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Stability of a class of multimedia systems
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Video segmentation using Metropolis Hastings Algorithm for the VCR operations
International Journal of Advanced Media and Communication
Character-based movie summarization
Proceedings of the international conference on Multimedia
A fast image analysis technique for the line tracking robots
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Learning relations among movie characters: a social network perspective
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Video scene detection using graph-based representations
Image Communication
Scene segmentation in artistic archive documentaries
USAB'10 Proceedings of the 6th international conference on HCI in work and learning, life and leisure: workgroup human-computer interaction and usability engineering
An unsupervised approach for recurrent tv program structuring
Proceddings of the 9th international interactive conference on Interactive television
Dominant sets based movie scene detection
Signal Processing
Don't ask me what i'm like, just watch and listen
Proceedings of the 20th ACM international conference on Multimedia
A general Framework of video segmentation to logical unit based on conditional random fields
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Application of 3D-wavelet statistics to video analysis
Multimedia Tools and Applications
Unsupervised text segmentation using LDA and MCMC
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Multimodal late fusion bag of features applied to scene detection
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Videos are composed of many shots that are caused by different camera operations, e.g., on/off operations and switching between cameras. One important goal in video analysis is to group the shots into temporal scenes, such that all the shots in a single scene are related to the same subject, which could be a particular physical setting, an ongoing action or a theme. In this paper, we present a general framework for temporal scene segmentation in various video domains. The proposed method is formulated in a statistical fashion and uses the Markov chain Monte Carlo (MCMC) technique to determine the boundaries between video scenes. In this approach, a set of arbitrary scene boundaries are initialized at random locations and are automatically updated using two types of updates: diffusion and jumps. Diffusion is the process of updating the boundaries between adjacent scenes. Jumps consist of two reversible operations: the merging of two scenes and the splitting of an existing scene. The posterior probability of the target distribution of the number of scenes and their corresponding boundary locations is computed based on the model priors and the data likelihood. The updates of the model parameters are controlled by the hypothesis ratio test in the MCMC process, and the samples are collected to generate the final scene boundaries. The major advantage of the proposed framework is two-fold: 1) it is able to find the weak boundaries as well as the strong boundaries, i.e., it does not rely on the fixed threshold; 2) it can be applied to different video domains. We have tested the proposed method on two video domains: home videos and feature films, and accurate results have been obtained