| Full text | |
| Author(s): |
Ferreira, Alvaro R., Jr.
;
de Rosa, Gustavo H.
;
Papa, Joao P.
;
Carneiro, Gustavo
;
Faria, Fabio A.
;
IEEE COMP SOC
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 8-pg., 2021-01-01. |
| Abstract | |
Convolutional Neural Networks (CNN) have been being widely employed to solve the challenging remote sensing task of aerial scene classification. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the development of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios. Our results suggest that the Univariate Marginal Distribution Algorithm shows more effective and efficient results than other commonly used meta-heuristic algorithms, such as Genetic Programming and Particle Swarm Optimization. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/23908-1 - Towards the Robustness in Deep Learning Architectures for e-Science Applications |
| Grantee: | Fabio Augusto Faria |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 19/02205-5 - Adversarial learning in natural language processing |
| Grantee: | Gustavo Henrique de Rosa |
| Support Opportunities: | Scholarships in Brazil - Doctorate |