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arXiv · 1902.03377

Region based Ensemble Learning Network for Fine-grained Classification

Weikuang Li·Tian Wang·Chuanyun Wang·Guangcun Shan·Mengyi Zhang·Hichem Snoussi
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Abstract

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.

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Weikuang Li, Tian Wang, Chuanyun Wang, Guangcun Shan, Mengyi Zhang, Hichem Snoussi. 2019-02-09. Region based Ensemble Learning Network for Fine-grained Classification. https://doi.org/10.1109/cac.2018.8623687

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