Abstract
Multilevel image thresholding is an important technique for image processing. However, the computational complexity of multilevel image thresholding grows exponentially with the increase in the number of thresholds when using the exhaustive searching method. To address this problem, a plenty of heuristic algorithms are applied to search the optimal thresholds. In this paper, an improved flower pollination algorithm (IFPA) using Tsallis entropy as its objective function is presented to find the optimal multilevel thresholding. In the IFPA, three modifications are utilized to enhance the flower pollination algorithm (FPA). First, an adaptive switch probability method is used to balance the local and global pollination. Second, a new local pollination strategy is adopted to avoid the population falling into local optimum. Third, an crossover and selection operations are applied to the FPA which can increase the diversity of the population, then enhancing the performance of the FPA. Subsequently, three different algorithms such as FPA, GSA and DE are introduced to compare with the IFPA in the experiments. The experimental results demonstrated that the IFPA can search out the optimal thresholds effectively, accurately and can obtain the best image segmentation quality.
Highlights
Image segmentation is used to extract the section of interest from an acquired image which is regarded as a pretreatment step in image processing [1]
Rodrigues et al developed a binary flower pollination algorithm (BFPA) for feature selection, the results showed that BFPA provided better results than particle swarm optimization (PSO), HSA and firefly algorithm (FA) [25]
In this paper, an improved flower pollination algorithm is proposed for multilevel thresholding image segmentation
Summary
Image segmentation is used to extract the section of interest from an acquired image which is regarded as a pretreatment step in image processing [1]. TAN: Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding algorithms are applied to solve various optimization problems. Compared with other heuristic algorithms, the FPA shows good results for solving various real-life optimization problems from different field such as global optimization problems [19], wireless sensor network [20], feature selection [21], multiple objective problems [22] and many others [23], [24]. Metwalli et al proposed a chaosbased flower pollination algorithm (CFPA) to solve fractional programming problems in which the chaos theory in the enhancement of local search, the experimental results showed that the performance of CFPA is better than others algorithms [26]. Shen et al presented a modified FPA (MFPA) to searching the optimal multilevel thresholding Their proposed method modified the local and global pollination, the simulation results demonstrated the superiority of the MFPA [27].
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