As an important research topic in computer vision, fine-grained classification which aims to recognition subordinate-level categories has attracted significant attention. We propose a novel region based ensemble learning network for fine-grained classification. Our approach contains a detection module and a module for classification. The detection module is based on the faster R-CNN framework to locate the semantic regions of the object. The classification module using an ensemble learning method, which trains a set of sub-classifiers for different semantic regions and combines them together to get a stronger classifier. In the evaluation, we implement experiments on the CUB-2011 dataset and the result of experiments proves our method s efficient for fine-grained classification. We also extend our approach to remote scene recognition and evaluate it on the NWPU-RESISC45 dataset.
@article{arxiv.1902.03377,
title = {Region based Ensemble Learning Network for Fine-grained Classification},
author = {Weikuang Li and Tian Wang and Chuanyun Wang and Guangcun Shan and Mengyi Zhang and Hichem Snoussi},
journal= {arXiv preprint arXiv:1902.03377},
year = {2019}
}
Comments
6 pages, 3 figures, 2018 Chinese Automation Congress (CAC)