Abstract:

In order to overcome the issue of large amount of calculation and long computing time of the traditional multi-threshold image segmentation methods， a multi-threshold image segmentation method based on improved cuckoo search algorithm is proposed. Firstly， the teaching-learning search strategy is introduced into the cuckoo algorithm to improve local search ability of the algorithm. Secondly， the elite solution with better fitness value in the current population is selected to construct the elite database， and the elite solution is randomly selected to guide the search direction， so as to strengthen the advantage of experience learning. Finally， the simulated annealing mechanism is introduced to select the bird's nest location， which can effectively avoid the individuals falling into the local optimum in the process of optimization. Several different types of complex multi-target images are selected for segmentation experiments in comparison with those of cuckoo search algorithm （CS）， shuffled frog leaping algorithm （SFL）， teaching-learning-based optimization （TLBO） and hybrid PSOGSA with generalized oppositing-based learning （GOPSOGSA）. Experimental results show that the proposed method is superior to the contrasted algorithms in segmentation accuracy， running time and convergence. It can quickly and effectively solve the multi-threshold segmentation problem of complex multi-target images.

Key words: image segmentation, multilevel threshold segmentation, cuckoo search algorithm（CS）, teaching-learning search strategy, elite solution, simulated annealing mechanism