@Article{cmc.2020.012161,
AUTHOR = {Rong Duan, Junshan Tan, Jiaohua Qin, Xuyu Xiang, Yun Tan, Neal N. Xiong},
TITLE = {Multi-Index Image Retrieval Hash Algorithm Based on Multi-View Feature Coding},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2335--2350},
URL = {http://www.techscience.com/cmc/v65n3/40173},
ISSN = {1546-2226},
ABSTRACT = {In recent years, with the massive growth of image data, how to match the 
image required by users quickly and efficiently becomes a challenge. Compared with 
single-view feature, multi-view feature is more accurate to describe image information. 
The advantages of hash method in reducing data storage and improving efficiency also 
make us study how to effectively apply to large-scale image retrieval. In this paper, a 
hash algorithm of multi-index image retrieval based on multi-view feature coding is 
proposed. By learning the data correlation between different views, this algorithm uses 
multi-view data with deeper level image semantics to achieve better retrieval results. This 
algorithm uses a quantitative hash method to generate binary sequences, and uses the 
hash code generated by the association features to construct database inverted index files, 
so as to reduce the memory burden and promote the efficient matching. In order to reduce 
the matching error of hash code and ensure the retrieval accuracy, this algorithm uses 
inverted multi-index structure instead of single-index structure. Compared with other 
advanced image retrieval method, this method has better retrieval performance.},
DOI = {10.32604/cmc.2020.012161}
}