Automated selection of parameters using Tabu Search in image segmentation

Segmentation is a process to obtain the desirable features in image processing. However, the existing techniques that use the multilevel thresholding method in image segmentation are computationally demanding due to the lack of an automatic parameter selection process. This paper proposesan automatic parameter selection technique called an automatic multilevel thresholding algorithm using stratified sampling and Tabu Search (AMTSSTS) to remedy thelimitations. It automatically determines the appropriatethreshold number and values by (1) dividing an image intoeven strata (blocks) to extract samples; (2) applying a Tabu Search-based optimization technique on these samples tomaximize the ratios of their means and variances; (3)preliminarily determining the threshold number and valuesbased on the optimized samples; and (4) further optimizingthese samples using a novel local criterion function thatcombines with the property of local continuity of an image. Experiments on Berkeley datasets show that AMTSSTS is an efficient and effective technique which can provide smoother results than several developed methods in recent years.