Name
Title | ||
|---|---|---|
Fast Content-Based Image Retrieval Using Convolutional Neural Network And Hash Function |
Abstract | ||
|---|---|---|
Due to the explosive increase of online images, content-based image retrieval has gained a lot of attention. The success of deep learning techniques such as convolutional neural networks have motivated us to explore its applications in our context. The main contribution of our work is a novel endto- end supervised learning framework that learns probability-based semantic-level similarity and feature-level similarity simultaneously. The main advantage of our novel hashing scheme that it is able to reduce the computational cost of retrieval significantly at the state-of-the-art efficiency level. We report on comprehensive experiments using public available datasets such as Oxford, Holidays and ImageNet 2012 retrieval datasets. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Data mining,Convolutional code,Computer science,Convolutional neural network,Image retrieval,Supervised learning,Artificial intelligence,Hash function,Deep learning,Artificial neural network,Machine learning,Content-based image retrieval |
DocType | ISSN | Citations |
Conference | 1062-922X | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Domonkos Varga | 1 | 13 | 4.29 |
| Tamás Szirányi | 2 | 152 | 26.92 |