Deep Discrete Hashing With Self-supervised Pairwise Labels
2017 · Jingkuan Song, Tao He, Hangbo Fan, et al.
Abstract
Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimen
Authors
(none)
Tags
Stats
Related papers
- SSDH: Semi-supervised Deep Hashing For Large Scale Image Retrieval (2016)15.40
- Deep Supervised Discrete Hashing (2017)0.00
- Deep Self-adaptive Hashing For Image Retrieval (2021)8.35
- Deep Discrete Supervised Hashing (2017)15.06
- Supervised Deep Hashing For Hierarchical Labeled Data (2017)8.09
- Improved Deep Hashing With Soft Pairwise Similarity For Multi-label Image Retrieval (2018)16.82
- Hierarchy Neighborhood Discriminative Hashing For An Unified View Of Single-label And Multi-label Image Retrieval (2019)0.00
- Deep Residual Hashing (2016)0.00