Open Access
ARTICLE
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. https://doi.org/10.32604/cmc.2020.09857
Received 22 January 2020; Accepted 07 August 2020; Issue published 16 September 2020
Abstract
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.
Keywords
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.