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Enhance Feature Discrimination for Unsupervised Hashing

Abstract · Apr 6, 2017 08:58 ·

cs-cv cs-ir

Arxiv Abstract

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

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

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