Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Scene Segmentation and Image Feature Extraction for Video Indexing and Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
A Hierarchiacal Approach to Scene Segmentation
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Highlight scene extraction in real time from baseball live video
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
A Novel Key-Frame Detection Technique Using Statistical Run Test and Majority Voting
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Scene Determination Based on Video and Audio Features
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Video scene segmentation and semantic representation using a novel scheme
Multimedia Tools and Applications
Scene detection in videos using shot clustering and sequence alignment
IEEE Transactions on Multimedia
Shot boundary detection using frame transition parameters and edge strength scatter
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Shot clustering techniques for story browsing
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Detection and representation of scenes in videos
IEEE Transactions on Multimedia
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Bag of visual words model for videos segmentation into scenes
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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In this paper, we present a novel scheme for segmenting video data into scenes. Based on visual similarity, the shots are first classified into clusters using modified k-means algorithm. Number of optimal clusters is decided using cluster validity analysis based on Davies-Bouldin index. Each shot is assigned a tag denoting the cluster it belongs to. Thus, the video data is represented by a sequence of cluster tags. The sequence is then analyzed by introducing the concept of stable and quasi-stable state. The elements of the sequence are merged into states and isolated elements are linked with the states to generate the scenes. The scheme is free from the dependency on critical parameters and capable of handling different types of scenes.