%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.11.14.00
%2 sid.inpe.br/sibgrapi/2016/07.11.14.00.03
%@doi 10.1109/SIBGRAPI.2016.058
%T A Meta-learning Approach for Recommendation of Image Segmentation Algorithms
%D 2016
%A Campos, Gabriel F. C.,
%A Mantovani, Rafael G.,
%A Jr., Sylvio Barbon,
%@affiliation Londrina State University (UEL)
%@affiliation University of Sao Paulo (USP)
%@affiliation Londrina State University (UEL)
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K Segmentation algorithm recommendation, metalearning, image processing.
%X 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 meta-models were able to recommend segmentation algorithms with high predictive performance.
%@language en
%3 PID4348117.pdf