A meta-learning approach for selecting image segmentation algorithm

•Automated selection of image segmentation algorithms.•Computational cost reduction in image segmentation tasks.•Meta-learning outperformed any single algorithm explored over the whole dataset.•Machine Learning and Gradient-based image segmentation algorithms were studied.•Experiments were conducted...

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Bibliographic Details
Published in:Pattern recognition letters Vol. 128; pp. 480 - 487
Main Authors: Aguiar, Gabriel Jonas, Mantovani, Rafael Gomes, Mastelini, Saulo M., de Carvalho, André C.P.F.L., Campos, Gabriel F.C., Junior, Sylvio Barbon
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2019
Elsevier Science Ltd
Subjects:
Algorithm recommendation
Algorithms
Artificial intelligence
Computer applications
Cybernetics
Gradient-based techniques
Image processing
Image segmentation
Learning algorithms
Machine learning
Meta-learning
Image segmentation
Algorithm recommendation
Gradient-based techniques
Meta-learning
ISSN:0167-8655, 1872-7344
Online Access:Get full text
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Summary:•Automated selection of image segmentation algorithms.•Computational cost reduction in image segmentation tasks.•Meta-learning outperformed any single algorithm explored over the whole dataset.•Machine Learning and Gradient-based image segmentation algorithms were studied.•Experiments were conducted over a widely known image dataset with 2100 examples. [Display omitted] 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.
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ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2019.10.018