A meta-learning approach for selecting image segmentation algorithm
Copyright policy )A meta-learning approach for selecting image segmentation algorithm
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.
- UNIVERSIDADE DE SAO PAULO Brazil
- Federal University of Technology – Paraná Brazil
- Universidade de São Paulo Brazil
- University of Trieste Italy
- Londrina State University Brazil
Image segmentation, Meta-learning, Meta-learning; Image segmentation; Gradient-based techniques; Algorithm recommendation, Algorithm recommendation, Gradient-based technique
Image segmentation, Meta-learning, Meta-learning; Image segmentation; Gradient-based techniques; Algorithm recommendation, Algorithm recommendation, Gradient-based technique
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