Name
Abstract | ||
|---|---|---|
Hashing based multimedia retrieval methods have been widely studied because of the advantages of computation efficiency and low storage cost. Generally, there are two ways of doing hashing: Supervised and unsupervised hashing. Supervised hashing was known to be more efficient and popular. However, in many real applications, only unsupervised method work because there are no labels. But unsupervised hashing method still face some challenges such as how to get semantic information from unlabeled data. To address this problem, in this paper, we propose a deep unsupervised hashing method based on semantic segmentation (Seg-HashNet). Specifically, a pre-trained semantic segmentation model is used to mine informative semantic information to guide the learning process and generate high-quality hash codes. Extensive experiments on two public datasets have been performed, which showed that Unsupervised Semantic Segmentation hashing is superior to the latest unsupervised hashing method in image retrieval tasks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190670 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
Multimedia Retrieval, Hashing, Semantic Segmentation, Deep Neural Network | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Zhang | 1 | 0 | 0.34 |
| Song Liu | 2 | 6 | 2.44 |
| Zhu Yuesheng | 3 | 112 | 39.21 |
| Zhiqiang Bai | 4 | 0 | 0.34 |
| Jinlong Lin | 5 | 22 | 6.11 |