caa a22 4500 605468834 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1425-3 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1425-3 Automatic multilevel thresholding for image segmentation using stratified sampling and Tabu Search [Elektronische Daten] [Yunzhi Jiang, Pohsiang Tsai, Zhifeng Hao, Longbing Cao] Image segmentation techniques have been widely applied in many fields such as pattern recognition and feature extraction. For the primate visual attention model, the perceptual organization is an important process to automatically extract the desirable features. In this article, we propose a new method called an automatic multilevel thresholding algorithm using the stratified sampling and Tabu Search (AMTSSTS) by imitating the primate visual perceptual behaviors. In the AMTSSTS algorithm, a gray image is treated as a population with the gray values of pixels as the individuals. First, the image is evenly divided into several strata (blocks), and a sample is drawn from each stratum. Second, a Tabu Search-based optimization is applied to each sample to maximize the ratio between mean and variance for each sample. The threshold number and threshold values are preliminarily determined based on the optimized samples, and are further optimized by a deterministic method which includes a new local criterion function with property of local continuity of an image. Results of extensive simulations on Berkeley datasets indicate that AMTSSTS can obtain more effective, efficient and smooth segmentation, and can be applied to complex and real-time environments. Springer-Verlag Berlin Heidelberg, 2014 Image segmentation nationallicence Multilevel thresholding nationallicence Intelligent optimization algorithms nationallicence Stratified sampling nationallicence Tabu Search nationallicence Jiang Yunzhi School of Software, Jiangxi Agricultural University, Nanchang, China aut Tsai Pohsiang Department of Computer Science and Information Engineering, National Formosa University, Huwei, Taiwan aut Hao Zhifeng School of Computer Science and Engineering, South China University of Technology, Guangzhou, China aut Cao Longbing Centre for Quantum Computation and Intelligent System, Advanced Analytics Institute, University of Technology, Sydney, Ultimo, Australia aut Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2605-2617 1432-7643 19:9<2605 2015 19 500 https://doi.org/10.1007/s00500-014-1425-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1425-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jiang Yunzhi School of Software, Jiangxi Agricultural University, Nanchang, China aut NATIONALLICENCE 700 1- Tsai Pohsiang Department of Computer Science and Information Engineering, National Formosa University, Huwei, Taiwan aut NATIONALLICENCE 700 1- Hao Zhifeng School of Computer Science and Engineering, South China University of Technology, Guangzhou, China aut NATIONALLICENCE 700 1- Cao Longbing Centre for Quantum Computation and Intelligent System, Advanced Analytics Institute, University of Technology, Sydney, Ultimo, Australia aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2605-2617 1432-7643 19:9<2605 2015 19 500