Research output per year
Research output per year
Thanh-Toan Do, Dang-Khoa Le Tan, Trung T. Pham, Ngai-Man Cheung
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subjected to a hashing function that produces a binary hash code. In previous work, the aggregating and the hashing processes are designed independently. In this paper, we propose a novel framework where feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization produces aggregated representations that can be better reconstructed by some binary codes. This leads to more discriminative binary hash codes and improved retrieval accuracy. In addition, we also propose a fast version of the recently-proposed Binary Autoencoder to be used in our proposed framework. We perform extensive retrieval experiments on several benchmark datasets with both SIFT and convolutional features. Our results suggest that the proposed framework achieves significant improvements over the state of the art.
Original language | English |
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Title of host publication | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
Editors | Yanxi Liu, James M. Rehg, Camillo J. Taylor, Ying Wu |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 4217-4226 |
Number of pages | 10 |
ISBN (Electronic) | 9781538604571 |
ISBN (Print) | 9781538604588 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2017 - Honolulu, United States of America Duration: 21 Jul 2017 → 26 Jul 2017 http://cvpr2017.thecvf.com/ https://ieeexplore.ieee.org/xpl/conhome/8097368/proceeding (Proceedings) |
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2017 |
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Abbreviated title | CVPR 2017 |
Country/Territory | United States of America |
City | Honolulu |
Period | 21/07/17 → 26/07/17 |
Internet address |
Research output: Contribution to journal › Article › Research › peer-review