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arXiv · 1704.01754

Enhance Feature Discrimination for Unsupervised Hashing

Tuan Hoang·Thanh-Toan Do·Dang-Khoa Le Tan·Ngai-Man Cheung
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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.

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Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. 2017-04-06. Enhance Feature Discrimination for Unsupervised Hashing. https://arxiv.org/abs/1704.01754

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