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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/354

Title: Classification of objects and background using parallel genetic algorithm based clustering
Authors: Kanungo, P
Nanda, P K
Ghosh, A
Samal, U C
Keywords: Segmentation
Genetic Algorithm
Genetic Algorithm
Clustering
Thersholding
Issue Date: 2006
Publisher: BCET, Hubli, India
Citation: Proceedings of the IEEE-International Conference on Signal and Image Processing, Dec 7-9, 2006, BCET, Hubli, India
Abstract: In this paper, a novel strategy based on the notion of threshold is proposed to accomplish segmentation of objects and background in a scene. Optimal threshold for two class and three classes problems are determined from the histogram of featured pixel values as opposed to the original normalized histogram. Genetic algorithm (GA) and Parallel Genetic Algorithm (PGA) based clustering algorithms are proposed to determine the optimal thresholds for two as well as three class problems. The optimal threshold could segment the noisy images. Our results, for two class problems, could be comparable with that of Otsu’s approach. Our approach yielded satisfactory results even for histograms having overlapping class distributions.
Description: Copyright for this article belongs to the publisher of the proceedings
URI: http://hdl.handle.net/2080/354
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