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Adaptive Binary Coding for Scene Classification Based on Convolutional Networks

Shuai Wang1, Xianyi Chen2, *
1 Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
2 Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, USA.
* Corresponding Author: Xianyi Chen. Email: .

Computers, Materials & Continua 2020, 65(3), 2065-2077.

Received 22 January 2020; Accepted 07 August 2020; Issue published 16 September 2020


With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed adaptive binary coding, our method is free of time to train or fine-tune the deep network and can effectively handle different applications. Experimental results on three public datasets, i.e., UIUC sports event dataset, MIT Indoor dataset, and UC Merced dataset in terms of three different classifiers, demonstrate that our method is superior to the state-of-the-art methods with large margins.


Scene classification, convolutional neural network, one-hot encoding, supervised feature training.

Cite This Article

S. Wang and X. Chen, "Adaptive binary coding for scene classification based on convolutional networks," Computers, Materials & Continua, vol. 65, no.3, pp. 2065–2077, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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