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Convolutional low-resolution fine-grained classification
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DOI：10.1016/j.patrec.2017.10.020.png)
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Abstract
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Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution. Encouraged by the recent success of Convolutional Neural Network (CNN) architectures in image classification, we propose a novel resolution-aware deep model which combines convolutional image super-resolution and convolutional fine-grained classification into a single model in an end-to-end manner. Extensive experiments on multiple benchmarks demonstrate that the proposed model consistently performs better than conventional convolutional networks on classifying fine-grained object classes in low-resolution images. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Fine-grained image classification
Super resolution convoluational neural networks
Deep learning
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