Name
Abstract | ||
|---|---|---|
For large-scale image retrieval, high dimensional features make the retrieval system inefficiency. In this paper, we propose a framework of deep feature hash codes for content-based image retrieval system. In this framework, we firstly extract image features by a pre-trained convolutional neural networks model. Secondly, we use different hashing methods for binary feature extraction. Finally, we use the best binary encoding features to build a content-based image retrieval system. The experimental results demonstrate that with the decrease of feature dimension, our method not only does not reduce the retrieval precision, but also can improve the retrieval accuracy in some cases. The retrieval accuracy of 256 bits binary features can surpass the traditional method of 256 dimensional (4096 bits) features. Once the feature bits are 16 times lower, the storage space will decrease 16 times and the retrieval efficiency will be greatly increased. Therefore, our method can effectively improve the speed and precision of content-based image retrieval system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WCSP.2016.7752525 | 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP) |
Keywords | Field | DocType |
image retrieval,convolutional neural networks,feature representation,hashing | Automatic image annotation,Pattern recognition,Feature detection (computer vision),Computer science,Feature (computer vision),Image retrieval,Feature extraction,Hash function,Artificial intelligence,Content-based image retrieval,Visual Word | Conference |
ISSN | ISBN | Citations |
2325-3746 | 978-1-5090-2861-0 | 0 |
PageRank | References | Authors |
0.34 | 18 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Li | 1 | 35 | 9.77 |
| Yulong Xu | 2 | 13 | 1.95 |
| Zhuang Miao | 3 | 23 | 7.51 |
| Hang Li | 4 | 2 | 3.09 |
| Jiabao Wang | 5 | 8 | 0.82 |
| Yafei Zhang | 6 | 14 | 1.57 |