Video Shot Boundary Detection by Dominant Sets Approach

In this study, a video shot boundary detection algorithm based on the dominant sets concept is proposed. Dominant sets method is a graph theoretic clustering algorithm. Proposed method is based on a weighted undirected graph. Candidate shot boundaries are determined and graphs are constructed by taking 2 frames from the right of the candidate position and 4 frames from the left of the candidate position. Edge weights among the vertices are evaluated by using pairwise similarities of frames. By using the complete information of the graph, a set of the vertices mostly similar to each other and dissimilar to the others is detected. True cut positions are determined if the dominant set includes the 4 frames before the candidate position. The simulation results indicate that the proposed algorithm can be used for abrupt shot boundary detection.


Video Shot Boundary Detection by Graph-theoretic Dominant Sets Approach
Asan, Emrah; Alatan, Abdullah Aydın (2009-09-16)
We present a video shot boundary detection algorithm based on the novel graph theoretic concept, namely dominant sets. Dominant sets are defined as a set of the nodes in a graph, mostly similar to each other and dissimilar to the others. In order to achieve this goal, candidate shot boundaries are determined by using simply pixelwise differences between consequent frames. For each candidate position, a testing sequence is constructed by considering 4 frames before the candidate position and 2 frames after t...
Video shot boundary detection by graph theoretic approaches
Aşan, Emrah; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2008)
This thesis aims comparative analysis of the state of the art shot boundary detection algorithms. The major methods that have been used for shot boundary detection such as pixel intensity based, histogram-based, edge-based, and motion vectors based, are implemented and analyzed. A recent method which utilizes “graph partition model” together with the support vector machine classifier as a shot boundary detection algorithm is also implemented and analyzed. Moreover, a novel graph theoretic concept, “dominant...
Radar target classification method with reduced aspect dependency and improved noise performance using multiple signal classification algorithm
SEÇMEN, MUSTAFA; Sayan, Gönül (Institution of Engineering and Technology (IET), 2009-12-01)
This study introduces a novel aspect and polarisation invariant radar target classification method based on the use of multiple signal classification (MUSIC) algorithm for feature extraction. In the suggested method, for each candidate target at each designated reference aspect, feature matrices called 'MUSIC spectrum matrices (MSMs)' are constructed using the target's scattered data at different late-time intervals. An individual MSM corresponds to a map of a target's natural resonance-related power distri...
Camera auto-calibration using a sequence of 2D images with small rotations
Hassanpour, R; Atalay, Mehmet Volkan (Elsevier BV, 2004-07-02)
In this study, we describe an auto-calibration algorithm with fixed but unknown camera parameters. We have modified Triggs' algorithm to incorporate known aspect ratio and skew values to make it applicable for small rotation around a single axis. The algorithm despite being a quadratic one is easy to solve. We have applied the algorithm to some artificial objects with known size and dimensions for evaluation purposes. In addition, the accuracy of the algorithm has been verified using synthetic data. The des...
Multimodal Stereo Vision Using Mutual Information with Adaptive Windowing
Yaman, Mustafa; Kalkan, Sinan (2013-05-23)
This paper proposes a method for computing disparity maps from a multimodal stereovision system composed of an infrared and a visible camera pair. The method uses mutual information (MI) as the basic similarity measure where a segmentation-based adaptive windowing mechanism is proposed for greatly enhancing the results. On several datasets, we show that (i) our proposal improves the quality of existing MI formulation, and (ii) our method can provide depth comparable to the quality of Kinect depth data.
Citation Formats
E. Asan and A. A. Alatan, “Video Shot Boundary Detection by Dominant Sets Approach,” 2009, Accessed: 00, 2020. [Online]. Available: