期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 29, 期 3, 页码 608-617出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2636870
关键词
Data-dependent hashing; least squares regression; supervised discrete hashing (SDH); supervised discrete hashing with relaxation (SDHR)
类别
资金
- National Science Foundation of China [61572463, 61573360]
- Open Project Program of the National Laboratory of Pattern Recognition (NLPR) [201700027]
- CCF-Tencent Open Fund
- Australian Research Council [DP-140102164, FT-130101457, LE-140100061]
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.
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