Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for high-dimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the pursued hash codes, which typically makes hash optimizations very challenging (NPhard in general). In this work, we propose a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm. One of the key steps in this algorithm is to solve a regularization sub-problem associated with the NP-hard binary optimization. We show that the sub-problem admits an analytical solution via cyclic coordinate descent. As such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, therefore enabling to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the stateof-the-art hashing methods in large-scale image retrieval.
Supervised discrete hashing (SDH) REF , where the learning objective is to generate the optimal binary hash code for linear classification, directly solved the corresponding discrete optimization without any relaxations.
Supervised Discrete Hashing
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science