A meta-learning approach for selecting image segmentation algorithm

Author(s)

Aguiar, Gabriel Jonas ^[1] ; Mantovani, Rafael Gomes ^{[2, 3]} ; Mastelini, Saulo M. ^[2] ; de Carvalho, Andre C. P. F. L. ^[2] ; Campos, Gabriel F. C. ^[1] ; Barbon Junior, Sylvio ^[1]

Affiliation

^[1] Univ Estadual Londrina, Comp Sci Dept, BR-86057970 Londrina, PR - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil

^[3] Fed Technol Univ Parana, BR-86812460 Apucarana, PR - Brazil

• Document type: Journal article
• Source: PATTERN RECOGNITION LETTERS; v. 128, p. 480-487, DEC 1 2019.
• Web of Science Citations: 0

Abstract

Image segmentation is a key issue in image processing. New image segmentation algorithms have been proposed in the last years. However, there is no optimal algorithm for every image processing task. The selection of the most suitable algorithm usually occurs by testing every possible algorithm or using knowledge from previous problems. These processes can have a high computational cost. Meta-learning has been successfully used in the machine learning research community for the recommendation of the most suitable machine learning algorithm for a new dataset. We believe that meta-learning can also be useful to select the most suitable image segmentation algorithm. This hypothesis is investigated in this paper. For such, we perform experiments with eight segmentation algorithms from two approaches using a segmentation benchmark of 300 images and 2100 augmented images. The experimental results showed that meta-learning can recommend the most suitable segmentation algorithm with more than 80% of accuracy for one group of algorithms and with 69% for the other group, overcoming the baselines used regarding recommendation and segmentation performance. (C) 2019 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 16/18615-0 - Advanced machine learning
• Grantee: André Carlos Ponce de Leon Ferreira de Carvalho
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 12/23114-9 - Use of meta-learning for parameter tuning for classification problems
• Grantee: Rafael Gomes Mantovani
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 18/07319-6 - Multi-target data stream mining
• Grantee: Saulo Martiello Mastelini
• Support Opportunities: Scholarships in Brazil - Doctorate