@Article{cmc.2020.09857,
AUTHOR = {Shuai Wang, Xianyi Chen},
TITLE = {Adaptive Binary Coding for Scene Classification Based on  Convolutional Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2065--2077},
URL = {http://www.techscience.com/cmc/v65n3/40155},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.09857}
}