A Meta-learning Approach for Recommendation of Image Segmentation Algorithms

Author(s)

Campos, Gabriel F. C. ; Barbon, Sylvio, Jr. ; Mantovani, Rafael G. ; IEEE

• Document type: Journal article
• Source: 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 8-pg., 2016-01-01.

Abstract

There are many algorithms for image segmentation, but there is no optimal algorithm for all kind of image applications. To recommend an adequate algorithm for segmentation is a challenging task that requires knowledge about the problem and algorithms. Meta-learning has recently emerged from machine learning research field to solve the algorithm selection problem. This paper applies meta-learning to recommend segmentation algorithms based on meta-knowledge. We performed experiments in four different meta-databases representing various real world problems, recommending when three different segmentation techniques are adequate or not. A set of 44 features based on color, frequency domain, histogram, texture, contrast and image quality were extracted from images, obtaining enough discriminative power for the recommending task in different segmentation scenarios. Results show that Random Forest metamodels were able to recommend segmentation algorithms with high predictive performance. (AU)

• 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