Automatic partitioning of full-motion video
Multimedia Systems
A feature-based algorithm for detecting and classifying scene breaks
Proceedings of the third ACM international conference on Multimedia
An efficient method for scene cut detection
Pattern Recognition Letters
A General Method for Shot Boundary Detection
MUE '08 Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering
Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines
Pattern Recognition Letters
Detection of hard cuts and gradual transitions from video using fuzzy logic
International Journal of Artificial Intelligence and Soft Computing
Shot Boundary Detection and Keyframe Extraction Based on Scale Invariant Feature Transform
ICIS '09 Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science
Novel automatic video cut detection technique using Gabor filtering
Computers and Electrical Engineering
Fuzzy color histogram-based video segmentation
Computer Vision and Image Understanding
Information theory-based shot cut/fade detection 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
Fast K-means algorithm based on a level histogram for image retrieval
Expert Systems with Applications: An International Journal
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Video shot boundary detection (SBD) is a fundamental step in automatic video content analysis toward video indexing, summarization and retrieval. Despite the beneficial previous works in the literature, reliable detection of video shots is still a challenging issue with many unsolved problems. In this paper, we focus on the problem of hard cut detection and propose an automatic algorithm in order to accurately determine abrupt transitions from video. We suggest a fuzzy rule-based scene cut identification approach in which a set of fuzzy rules are evaluated to detect cuts. The main advantage of the proposed method is that, we incorporate spatial and temporal features to describe video frames, and model cut situations according to temporal dependency of video frames as a set of fuzzy rules. Also, while existing cut detection algorithms are mainly threshold dependent; our method identifies cut transitions using a fuzzy logic which is more flexible. The proposed algorithm is evaluated on a variety of video sequences from different genres. Experimental results, in comparison with the most standard cut detection algorithms confirm our method is more robust to object and camera movements as well as illumination changes.