Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Approach to Grouping
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Summarizing audiovisual contents of a video program
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Movie scene segmentation using background information
Pattern Recognition
Fast communication: Dominant sets clustering for image retrieval
Signal Processing
Combining graph connectivity & dominant set clustering for video summarization
Multimedia Tools and Applications
Soccer Video Shot Classification Based on Color Characterization Using Dominant Sets Clustering
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Spectral structuring of home videos
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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
Spatio-temporal segmentation using dominant sets
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Computable scenes and structures in films
IEEE Transactions on Multimedia
Shot clustering techniques for story browsing
IEEE Transactions on Multimedia
Detection and representation of scenes in videos
IEEE Transactions on Multimedia
Video scene segmentation using Markov chain Monte Carlo
IEEE Transactions on Multimedia
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
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
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
Clip-based similarity measure for query-dependent clip retrieval and video summarization
IEEE Transactions on Circuits and Systems for Video Technology
A Formal Study of Shot Boundary Detection
IEEE Transactions on Circuits and Systems for Video Technology
Dominant sets based movie scene detection
Signal Processing
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
Video shot representation based on histograms
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Hi-index | 0.00 |
One of the fundamental steps in organizing videos is to parse it in smaller descriptive parts. One way of realizing this step is to obtain shot or scene information. One or more consecutive semantically correlated shots sharing the same content construct video scenes. On the other hand, video scenes are different from the shots in the sense of their boundary definitions; video scenes have semantic boundaries and shots are defined with physical boundaries. In this paper, we concentrate on developing a fast, as well as well-performed video scene detection method. Our graph partition based video scene boundary detection approach, in which multiple features extracted from the video, determines the video scene boundaries through an unsupervised clustering procedure. For each video shot to shot comparison feature, a one-dimensional signal is constructed by graph partitions obtained from the similarity matrix in a temporal interval. After each one-dimensional signal is filtered, an unsupervised clustering is conducted for finding video scene boundaries. We adopt two different graph-based approaches in a single framework in order to find video scene boundaries. The proposed graph-based video scene boundary detection method is evaluated and compared with the graph-based video scene detection method presented in literature.