SIGT: Synthetic image generation tool for clustering algorithms
Ayed Salman(1), Mahamed G Omran(2) and Andries P Engelbrecht(3)
(1)Department of Computer Engineering, Kuwait University, Kuwait, KUWAIT
(2,3)Department of Computer Science, University of Pretoria,
Pretoria, SOUTH AFRICA
Abstract
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A new automatic image generation tool is proposed in this paper tailored specifically for verification and comparison of different image clustering algorithms. The tool can be used to produce different images (in raw format) with different criteria based on user specification. The user specifies the number of clusters to be included in the image along with the probability distribution that governs a set of points that belong to different clusters. On the other hand, the tool can be used to verify the degree of approximation a new algorithm has been able to achieve compared to the original image. This allows for a scientific confident comparison between any new algorithm and existing algorithms. The tool usefulness is demonstrated in this paper with reference to the well-known K-means clustering algorithm and a Particle Swarm Optimization (PSO)-based clustering algorithm recently proposed by the authors.
Keywords: image clustering algorithms, K-means clustering algorithm, Particle Swarm Optimization (PSO).
Biographies:
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Ayed Salman
received his MS and PhD degrees from Syracuse University, NY, USA, in 1996 and 1999, respectively. He is an Assistant Professor at the Department of Computer Engineering, Kuwait University, Kuwait. His research interests include Application of evolutionary computations in data mining, and multi-objective optimization; Neural Network, Machine Learning, and Software agents. |
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Mahamed G Omran
received his BS and MS degrees in computer engineering from Kuwait University, Kuwait, in 1998 and 2000, respectively. Currently, Omran is studying for his PhD at the University of Pretoria, South Africa, in the area of swarm intelligence. Omran's research interests include computational intelligence and pattern recognition.
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Andries Engelbrecht received the M.Sc and PhD degrees from the University of Stellenbosch, South Africa, in 1994 and 1999, respectively. He is a Full Professor at the Department of Computer Science, University of Pretoria, Pretoria, South Africa. His research interests include aspects of swarm intelligence, evolutionary computation, artificial immune systems and neural networks, with several publications in those fields. He is the head of the Computational Intelligence Research Group, University of Pretoria with a group of 40 postgraduate students. Prof. Engelbrecht is a member of the IEEE NNS task forces on Evolutionary Computation and Games, Swarm Intelligence, and Coevolution, and chairs the task force on Data, Image and Web mining. He is the author of the book, "Computational Intelligence: An Introduction'', Wiley, 2002. |
BibTex:
@ARTICLE{P1150444002,
AUTHOR = {Ayed Salman and Mahamed G Omran and Andries P Engelbrecht }, TITLE = {SIGT: Synthetic image generation tool for clustering algorithms},
JOURNAL = {ICGST International Journal on Graphics, Vision and Image Processing},
YEAR = {2005},
MONTH={Jan.},
VOLUME = {05},
ISSUE={2},
PAGES={33--44}
}
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Full Paper
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