Supervised Discrete Hashing With Relaxation
Supervised Discrete Hashing With Relaxation
复制标题
放松的监督离散散列
DOI：
10.1109/tnnls.2016.2636870
复制
发表时间：
2018-03-01
影响因子：
10.4
通讯作者：
Tan, Tieniu
中科院分区：
文献类型：
--
作者：
Gui, Jie;Liu, Tongliang;Tan, Tieniu
Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called "supervised discrete hashing with relaxation" (SDHR) based on "supervised discrete hashing" (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.
