A fuzzy approach to evaluate image segmentation based on image complexity

Authors

  • Luis Madrid-Herrera Visual Perception Systems Lab, Tecnologico Nacional de Mexico/I.T Chihuahua, Chihuahua, Mexico https://orcid.org/0000-0001-9034-5384
  • Mario Ignacio Chacon-Murguia Visual Perception Systems Lab, Tecnologico Nacional de Mexico/I.T Chihuahua, Chihuahua, Mexico https://orcid.org/0000-0002-5382-9424
  • Juan Alberto Ramirez-Quintana Visual Perception Systems Lab, Tecnologico Nacional de Mexico/I.T Chihuahua, Chihuahua, Mexico https://orcid.org/0000-0003-4445-6555

Keywords:

fuzzy evaluation, image complexity, image segmentation evaluation

Abstract

Image segmentation evaluation may be carried out by comparing segmentation algorithm results with human ground truths. Its correct evaluation in benchmarks is important to promote the development of new and better segmentation algorithms. It may be partially considered a subjective task because each image may have multiple correct solutions. The evaluation is commonly carried out through crisp metrics, and they fail to generalize the subjectivity of the human criteria present in the human ground truths. Therefore, the interpretation and meaning of the metric values may be ambiguous. Thus, this paper presents a new fuzzy evaluation approach that considers the subjectivity of the human criteria by considering image complexity. This approach leads to an adequate evaluation of this subjective task. The proposed approach demonstrates its advantage when is evaluated using the Peng and SIHD meta-metrics achieving a performance of 0.795 and 0.971, respectively, outperforming the PRI, VI, GCE, and BDE metrics.

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Author Biographies

Luis Madrid-Herrera, Visual Perception Systems Lab, Tecnologico Nacional de Mexico/I.T Chihuahua, Chihuahua, Mexico

Received the B.S degree in mechatronics engineering from the Nuevo Casas Grandes Institute of Technology, Mexico, in 2014, and the M.Sc degree in electronic engineering from the Chihuahua Institute of Technology, México, in 2018. He is currently a Ph.D. student at the Chihuahua Institute of Technology in the Visual Perception Lab. His research interests include digital signal processing, machine learning, image processing, computer vision, visual perception, and pattern recognition.

Mario Ignacio Chacon-Murguia, Visual Perception Systems Lab, Tecnologico Nacional de Mexico/I.T Chihuahua, Chihuahua, Mexico

Received the B.Sc. (1982) and the M.Sc. (1985) degrees in Electrical Engineering from the Chihuahua Institute of Technology, Chihuahua, Mexico, and the Ph.D. (1998) in Electrical Engineering from New Mexico State University, USA. He is a Professor at the Chihuahua Institute of Technology, director of the Visual Perception Laboratory. He has published more than 218 works and published 3 books. He has directed 45 research projects for the industry and government institutions. His current research includes visual perception, image and signal processing using computational intelligence. He is an IEEE Senior member, and a member of the Mexican Research System.

Juan Alberto Ramirez-Quintana, Visual Perception Systems Lab, Tecnologico Nacional de Mexico/I.T Chihuahua, Chihuahua, Mexico

Received the B.Sc. (2004), the M.Sc. (2007) and Ph.D. (2014) degrees in electrical engineering from the Chihuahua Institute of Technology, Chihuahua, Chihuahua, Mexico. From 2008 to 2011 he was a research and teacher assistant in different universities. He currently works as a research-professor at Tecnologico Nacional de Mexico / Instituto Tecnologico de Chihuahua. His research interests include computer vision, signal processing, visual perception, computational intelligence and embedded systems. Dr. Juan A. Ramirez-Quintana is member of the National Research System in Mexico.

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Published

2022-12-21

How to Cite

Madrid-Herrera, L., Chacon-Murguia, M. I., & Ramirez-Quintana, J. A. (2022). A fuzzy approach to evaluate image segmentation based on image complexity. IEEE Latin America Transactions, 21(2), 260–269. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7110

Issue

Vol. 21 No. 2 (2023): Ordinary Issue

Section

Computing