preprint · arXiv (Cornell University) · 2017

Enhance Feature Discrimination for Unsupervised Hashing

Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai‐Man Cheung

Cited by 0Open access

Abstract

We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g. Binary Autoencoder (BA) [1], Iterative Quantization (ITQ) [2], in standard evaluation metrics for the three main benchmark datasets.

Cite this paper

APA 7

Hoang, T., Do, T.-T., Le Tan, D.-K., & Cheung, N. (2017). Enhance Feature Discrimination for Unsupervised Hashing. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1704.01754

MLA 9

Hoang, Tuan, et al. “Enhance Feature Discrimination for Unsupervised Hashing.” arXiv (Cornell University), 2017, https://doi.org/10.48550/arxiv.1704.01754.

Chicago

Hoang, Tuan, Thanh-Toan Do, Dang-Khoa Le Tan, and Ngai‐Man Cheung. 2017. “Enhance Feature Discrimination for Unsupervised Hashing.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1704.01754.

Harvard

Hoang, T. et al. (2017) ‘Enhance Feature Discrimination for Unsupervised Hashing’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1704.01754.

BibTeX

@misc{hoang2017enhance,
  title = {{Enhance Feature Discrimination for Unsupervised Hashing}},
  author = {Hoang, Tuan and Do, Thanh-Toan and Le Tan, Dang-Khoa and Cheung, Ngai‐Man},
  journal = {arXiv (Cornell University)},
  year = {2017},
  doi = {10.48550/arxiv.1704.01754}
}

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