LAUSR

Multilevel thresholding is to find the thresholds to segment the image with grey levels. Usually, the thresholds are so determined that some indicator functions of the segmented image are optimized. To improve the computational efficiency, we presented an optimization method for multilevel thresholding. First, the solution space is divided into subspaces. Second, the subspaces are searched to obtain their current local optimal value. Third, the subspaces that are of worse current optimal value are eliminated. Then, the next round of elimination is exerted in the remainder. The elimination is repeated until only one subspace is left and its optimal value is taken as the global optimum. In principle, any random search algorithm can be used to find the local optimum in a subspace block because it is a strategy to enhance the searching efficiency through eliminating hopeless regions as early as possible, rather than to improve the searching algorithm itself. To verify its performance, taking PSO (Particle swarm optimization) as the basic searching algorithm of subspaces, the presented method is applied to Otsu’s and Kapur’s multilevel thresholding of four different kinds of digital images. The presented method is compared with PSO, and it behaves better in efficiency.