@Article{cmc.2020.07730,
AUTHOR = {Junshan Tan, Rong Duan, Jiaohua Qin, Xuyu Xiang, Yun Tan},
TITLE = {Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {675--689},
URL = {http://www.techscience.com/cmc/v63n2/38537},
ISSN = {1546-2226},
ABSTRACT = {Hashing technology has the advantages of reducing data storage and improving 
the efficiency of the learning system, making it more and more widely used in image 
retrieval. Multi-view data describes image information more comprehensively than 
traditional methods using a single-view. How to use hashing to combine multi-view data 
for image retrieval is still a challenge. In this paper, a multi-view fusion hashing method 
based on RKCCA (Random Kernel Canonical Correlation Analysis) is proposed. In order 
to describe image content more accurately, we use deep learning dense convolutional 
network feature DenseNet to construct multi-view by combining GIST feature or 
BoW_SIFT (Bag-of-Words model+SIFT feature) feature. This algorithm uses RKCCA 
method to fuse multi-view features to construct association features and apply them to 
image retrieval. The algorithm generates binary hash code with minimal distortion error by 
designing quantization regularization terms. A large number of experiments on benchmark 
datasets show that this method is superior to other multi-view hashing methods.},
DOI = {10.32604/cmc.2020.07730}
}