Enhancing feature discrimination for unsupervised hashing

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing - Proceedings
EditorsJiebo Luo, Wenjun Zeng, Yu-Jin Zhang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3710-3714
Number of pages5
ISBN (Electronic)9781509021758, 9781509021741
ISBN (Print)9781509021765
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Conference on Image Processing 2017 - China National Convention Center (CNCC), Beijing, China
Duration: 17 Sep 201720 Sep 2017
Conference number: 24th
http://www.2017.ieeeicip.org/
http://2017.ieeeicip.org/

Publication series

NameProceedings - International Conference on Image Processing, ICIP
PublisherThe Institute of Electrical and Electronics Engineers Signal Processing Society
Volume2017-September
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing 2017
Abbreviated titleICIP 2017
CountryChina
CityBeijing
Period17/09/1720/09/17
Internet address

Keywords

  • Discrimination enhancement
  • Embedding
  • Gaussian mixture model
  • Hashing

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