web image retrieval
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Author(s):  
Nishu Garg ◽  
Nikhitha P ◽  
B. K. Tripathy

Information retrieval can be visualized as the extraction of the desired information from the flooded resources that spread over world wide web. Image retrieval is the fundamental and critical problem that arises in the retrieval activities. In this regard, it is considered to be a challenging task which requires utmost care. Diverse characteristics of data such as noisy, heterogeneity impose a great barrier over image retrieval applications. This chapter aims to come up with a state-of-the-art approach for overcoming these problems by clubbing together widely recognized deep architecture along with natural language processing. This novel design methodology utilizes the latent query features, deep belief network, restricted Boltzmann machine for learning tasks. This collaborative work can be used to reduce the epoch in the learning periods whereas the rest of the methods fail to achieve the constraints.


2018 ◽  
Vol 20 (9) ◽  
pp. 2371-2384
Author(s):  
Jianfeng Dong ◽  
Xirong Li ◽  
Duanqing Xu

2018 ◽  
Vol 430-431 ◽  
pp. 331-348 ◽  
Author(s):  
Changqin Huang ◽  
Haijiao Xu ◽  
Liang Xie ◽  
Jia Zhu ◽  
Chunyan Xu ◽  
...  

2016 ◽  
Vol 76 (20) ◽  
pp. 21401-21421
Author(s):  
Baopeng Zhang ◽  
Yanyun Qu ◽  
Jinye Peng ◽  
Jianping Fan

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