Fast Single-Parameter Energy Function Thresholding for Image Segmentation Based on Region Information

Rong Lan, Danlin Feng (), Feng Zhao, Jiulun Fan and Haiyan Yu
Additional contact information
Rong Lan: School of Communications and Information Engineering & School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Danlin Feng: School of Communications and Information Engineering & School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Feng Zhao: School of Communications and Information Engineering & School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Jiulun Fan: School of Communications and Information Engineering & School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
Haiyan Yu: School of Communications and Information Engineering & School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Mathematics, 2023, vol. 11, issue 4, 1-20

Abstract: To solve the problems of image threshold segmentation based on weak continuous constraint theory, the running time is long, and the two parameters need to be selected manually, and therefore a fast single-parameter energy function thresholding for image segmentation based on region information (FSEFTISRI) is proposed in this paper. The proposed FSEFTISRI algorithm uses simple linear iterative clustering (SLIC) technology to pre-block the image, extract the image super-pixels, and then map the image super-pixels to the interval type-2 fuzzy set (IT2FS), so as to construct the single-parameter energy function to search the optimal threshold, and adaptively select the penalty parameters in the energy function through the class uncertainty theory. On a non-destructive testing (NDT) database and Berkeley segmentation datasets and benchmarks (BSDS), the proposed FSEFTISRI is compared with five related algorithms. The average misclassification error (ME) of the proposed FSEFTISRI algorithm on NDT and BSDS are 0.0466 and 0.0039, respectively. The results show that the proposed FSEFTISRI has acquired more satisfactory results in visual effect and evaluation index, and the running time of the proposed FSEFTISRI algorithm is shorter, which shows the effectiveness of the proposed FSEFTISRI.

Keywords: class uncertainty theory; energy function; interval type-2 fuzzy set; super-pixel; threshold segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/4/1059/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/4/1059/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:4:p:1059-:d:1074540

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().