2019/04/07 by Jie Gui, Tongliang Liu, Gui, Jie +7
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Face recognition and analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Surveillance and Tracking Methods #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1904.03549
arxiv created 2019/04/07 · openalex publication_date 2019/04/07 · arxiv updated 2019/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 datasets (CIFAR-10 and MNIST) and a large-scale and challenging face dataset (FRGC) demonstrate the effectiveness and efficiency of SDHR.