Multi-Thresholding Based on Symmetric Tsallis-Cross Entropy and Particle Swarm Optimization

Abstract:

Multi-thresholding is an important step for automatic image analysis. In this paper, a multi-thresholding method based on symmetric Tsallis-cross entropy and uniform searching particle swarm optimization (UPSO) is proposed. The criterion function using symmetric Tsallis-cross entropy can make the grayscale within the background cluster and the object cluster uniform. Since the exhaustive multi-thresholding algorithm would be too time-consuming, UPSO algorithm is adopted to find the optimal thresholds quickly and accurately. A large number of experimental results show that, compared with related multi-thresholding methods based on Shannon entropy and Tsallis entropy, the proposed method is effective and rapid. It can obtain more accurate boundary shape and clearer details of object.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1457-1461
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1457
• Online since: September 2013
• Authors: Jun Yin, Yi Quan Wu, Li Zhu
• Keywords: Image Segmentation, Multi-Thresholding, Symmetric Tsallis-Cross Entropy, Uniform Searching Particle Swarm Optimization

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