Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computers & Geosciences - Intelligent methods for processing geodata
Feature Subset Selection Using Genetic Algorithms for Handwritten Digit Recognition
SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
Machine learning for adaptive image interpretation
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Genetic algorithms for action set selection across domains: a demonstration
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Short communication: An evaluation metric for image segmentation of multiple objects
Image and Vision Computing
| Hi-index | 0.00 |
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an extensive generic image processing operator library. One of the principal weaknesses of the method lies with the large size of such libraries, which can make the machine learning process intractable. We demonstrate how evolutionary algorithms can be used to reduce the size of the operator library, thereby speeding up learning of the policy while still keeping human experts out of the development loop. Experiments in a challenging domain of forestry image interpretation exhibited a 95% reduction in the average time required to interpret an image, while maintaining the image interpretation accuracy of the full library.