Otsu’s Between Class Variance and the Tree Seed Algorithm

Abstract

In this chapter, an alternative image segmentation method is proposed to find the multi-level thresholding values. The proposed method is based on the tree seed algorithm (TSA) which emulates the tree generations. The proposed TSA used the maximum between class variance criterion (Otsu) as a fitness function. In order to evaluate the performance of the proposed method a set of images are used and the results and compared with other methods. The experimental results illustrated that the proposed TSA method has better performance to determine the multi-level thresholding problem for image segmentation than other methods regarding performance measures.

Original language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Verlag
Pages	71-83
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-030-12931-6_7
Publication status	Published - 2019
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	825
ISSN (Print)	1860-949X

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.1007/978-3-030-12931-6_7

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Oliva, D., Abd Elaziz, M., & Hinojosa, S. (2019). Otsu’s Between Class Variance and the Tree Seed Algorithm. In Studies in Computational Intelligence (pp. 71-83). (Studies in Computational Intelligence; Vol. 825). Springer Verlag. https://doi.org/10.1007/978-3-030-12931-6_7

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