To address the problems of low segmentation accuracy and poor adaptability of existing multi-threshold segmentation methods for color images,a multi-threshold segmentation method with high accuracy and adaptability based on circular histogram linearization is proposed.After superpixel preprocessing of the input color image,the method first constructs the cumulative distribution variance maximization criterion,based on which the circular histogram is truncated and extended into a linear histogram.Thereafter,a new multi-threshold segmentation objective function is constructed by combining the between-class variance and Tsallis entropy on the linear histogram.Finally,the Sparrow Search Algorithm(SSA)is introduced to quickly solve the multi-threshold segmentation objective function to obtain the optimal threshold.On eight synthetic images and 500 real world images,the proposed method is comprehensively compared with nine different color image segmentation methods.The comprehensive experimental results on six quantitative evaluation indicators,such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Feature Similarity Index Measure(FSIM),Probabilistic Rand Index(PRI),Global Consistency Error(GSE),and Variation of Information(VI),show that,the proposed method is approximately equal to the compared method in computational efficiency,but it is significantly better than the compared nine methods in segmentation accuracy and adaptability.The proposed method is ranked first in terms of PSNR(19.95 dB),SSIM(0.80),FSIM(0.94),and GSE(0.16).
multi-threshold segmentationcircular histogramcumulative distribution varianceadaptive Tsallis entropySparrow Search Algorithm(SSA)
国家科技期刊平台
NSTL
维普
万方数据
