Abstract
We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g. Binary Autoencoder (BA) [1], Iterative Quantization (ITQ) [2], in standard evaluation metrics for the three main benchmark datasets.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Image Processing - Proceedings |
| Editors | Jiebo Luo, Wenjun Zeng, Yu-Jin Zhang |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 3710-3714 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509021758, 9781509021741 |
| ISBN (Print) | 9781509021765 |
| DOIs | |
| Publication status | Published - 2017 |
| Externally published | Yes |
| Event | IEEE International Conference on Image Processing 2017 - China National Convention Center (CNCC), Beijing, China Duration: 17 Sept 2017 → 20 Sept 2017 Conference number: 24th http://www.2017.ieeeicip.org/ http://2017.ieeeicip.org/ https://ieeexplore.ieee.org/xpl/conhome/8267582/proceeding (Proceedings) |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| Publisher | The Institute of Electrical and Electronics Engineers Signal Processing Society |
| Volume | 2017-September |
| ISSN (Print) | 1522-4880 |
Conference
| Conference | IEEE International Conference on Image Processing 2017 |
|---|---|
| Abbreviated title | ICIP 2017 |
| Country/Territory | China |
| City | Beijing |
| Period | 17/09/17 → 20/09/17 |
| Internet address |
Keywords
- Discrimination enhancement
- Embedding
- Gaussian mixture model
- Hashing