Otsu’s Between Class Variance and the Tree Seed Algorithm

Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

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 languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages71-83
Number of pages13
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume825
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    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