Deep LDA Hashing

Abstract

The conventional supervised hashing methods based on classification do not entirely meet the requirements of hashing technique, but Linear Discriminant Analysis (LDA) does. In this paper, we propose to perform a revised LDA objective over deep networks to learn efficient hashing codes in a truly end-to-end fashion. However, the complicated eigenvalue decomposition within each mini-batch in every epoch has to be faced with when simply optimizing the deep network w.r.t. the LDA objective. In this work, the revised LDA objective is transformed into a simple least square problem, which naturally overcomes the intractable problems and can be easily solved by the off-the-shelf optimizer. Such deep extension can also overcome the weakness of LDA Hashing in the limited linear projection and feature learning. Amounts of experiments are conducted on three benchmark datasets. The proposed Deep LDA Hashing shows nearly 70 points improvement over the conventional one on the CIFAR-10 dataset. It als

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyhu2018deep

Related papers

• Deep Discrete Hashing With Self-supervised Pairwise Labels (2017)9.49
• Deep Discrete Supervised Hashing (2017)15.06
• Deep Residual Hashing (2016)0.00
• Deep Reinforcement Learning For Image Hashing (2018)12.25
• Deep Supervised Discrete Hashing (2017)0.00
• Deep Class-wise Hashing: Semantics-preserving Hashing Via Class-wise Loss (2018)11.19
• Supervised Deep Hashing For Hierarchical Labeled Data (2017)8.09
• Unsupervised Semantic Deep Hashing (2018)10.48