Bilinear Supervised Hashing Based On 2D Image Features
2019 · Yujuan Ding, Wai Kueng Wong, Zhihui Lai, et al.
Abstract
Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. However, studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. This paper proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the 2D image space are preserved in the learned binary codes. Meanwhile, the bilinear projection approximation and vectorization binary codes regression are seamlessly integrated together to formulate the final robust learning framework. Furthermore, a discrete optimization strategy is developed to alternatively update each variable for
Authors
(none)
Tags
Stats
Related papers
- Simultaneous Feature Aggregating And Hashing For Compact Binary Code Learning (2019)9.92
- Deep Hashing: A Joint Approach For Image Signature Learning (2016)5.24
- Deep Discrete Hashing With Self-supervised Pairwise Labels (2017)9.49
- SSDH: Semi-supervised Deep Hashing For Large Scale Image Retrieval (2016)15.40
- Unsupervised Deep Hashing For Large-scale Visual Search (2016)9.59
- Deep Supervised Discrete Hashing (2017)0.00
- Deep Residual Hashing (2016)0.00
- Supervised Hashing With End-to-end Binary Deep Neural Network (2017)2.26