基于深度线性判别分析的哈希技术

The conventional supervised hashing methods based on classification do not entirely meet the requirements of the hashing technique; on the other hand, the linear discriminant analysis (LDA) approach does satisfy such demands. In this paper, we propose to perform the LDA objective over deep networks to learn efficient hashing codes in a truly end-to-end fashion. However, a complicated eigenvalue decomposition within each mini-batch in every epoch has to be faced with when simply optimizing the deep network with respect to the LDA objective. Here, the LDA objective is transformed into a simple least-square problem, which naturally overcomes the intractable problems and can be easily solved by an off-the-shelf optimizer. Such deep extension can also overcome the weaknesses of LDA hashing involving the limited linear projection and feature learning. Numerous experiments were conducted on three benchmark datasets. The proposed deep LDA hashing approach exhibits an improvement of nearly 70 points for the CIFAR-10 dataset over the conventional strategy. Additionally, the proposed approach is found to be superior to several state-of-the-art methods for various metrics.