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R2SDH：鲁棒旋转监督离散散列R2SDH: Robust Rotated Supervised Discrete Hashing |
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| 课程网址: | http://videolectures.net/kdd2018_li_robust_hashing/ |
| 主讲教师: | Piotr Rudol |
| 开课单位: | 林克平大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Learning-based hashing has recently received considerable attentions due to its capability of supporting efficient storage and retrieval of high-dimensional data such as images, videos, and documents. In this paper, we propose a learning-based hashing algorithm called “Robust Rotated Supervised Discrete Hashing” (R 2 SDH), by extending the previous work on “Supervised Discrete Hashing” (SDH). In R 2 SDH, correntropy is adopted to replace the least square regression (LSR) model in SDH for achieving better robustness. Furthermore, considering the commonly used distance metrics such as cosine and Euclidean distance are invariant to rotational transformation, rotation is integrated into the original zero-one label matrix used in SDH, as additional freedom to promote flexibility without sacrificing accuracy. The rotation matrix is learned through an optimization procedure. Experimental results on three image datasets (MNIST, CIFAR-10, and NUS-WIDE) confirm that R 2 SDH generally outperforms SDH. |
| 关 键 词: | 基于学习的哈希; 鲁棒旋转监督离散哈希; 最小二乘回归; 图像数据集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-29：cyh |
| 最后编审: | 2023-01-30：cyh |
| 阅读次数: | 14 |