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ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING

Citation Author(s):
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung
Submitted by:
Tuan Hoang
Last updated:
14 September 2017 - 11:24am
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Tuan Hoang
Paper Code:
3071
 

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), Iterative Quantization (ITQ), in standard evaluation metrics for the three main benchmark datasets.

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