Title :
A Threshold Segmentation Method for Sparse Histogram Image
Author :
Zhang, Hong ; Fan, Jiulun
Author_Institution :
Sch. of Electron. Eng., Xidian Univ., Xi´´an, China
Abstract :
Maximum entropy thresholding method is a common image segmentation technology, several optimization algorithms are proposed based on maximum entropy objective function, these algorithms use subtraction instead of logarithm and multiplication and which are used in image segmentation. But for sparse histogram images, the segmentation based on the existing optimize methods is ineffective. In this paper, for sparse histogram image segmentation, an improved maximum entropy optimization algorithm is presented. Sparse histogram image segmentation experimental results show that more reasonable segmentation results can be obtained through using the algorithm.
Keywords :
image segmentation; maximum entropy methods; optimisation; common image segmentation technology; maximum entropy objective function; optimization algorithms; sparse histogram image; threshold segmentation method; Computational complexity; Control systems; Entropy; Fuzzy systems; Histograms; Image segmentation; Knowledge engineering; Optimization methods; Telecommunication computing; Telecommunication control; algorithm; image segmentation; maximum entropy; optimization; sparse histogram;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
DOI :
10.1109/FSKD.2009.557