Learning deep unsupervised binary codes for image retrieval

Junjie Chen, Kwok Wai CHEUNG, Anran Wang

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

15 Citations (Scopus)


Hashing is an efficient approximate nearest neighbor search method. It has been widely adopted for large-scale multimedia retrieval. While supervised learning is popular for the data-dependent hashing, deep unsupervised hashing methods can learn non-linear transformations for converting multimedia inputs to binary codes without label information. Most of the existing deep unsupervised hashing methods make use of a quadratic constraint for minimizing the difference between the compact representations and the target binary codes, which inevitably causes severe information loss. In this paper, we propose a novel deep unsupervised method called DeepQuan for hashing. The DeepQuan model utilizes a deep autoencoder network, where the encoder is used to learn compact representations and the decoder is for manifold preservation. To contrast with the existing unsupervised methods, DeepQuan learns the binary codes by minimizing the quantization error through product quantization. Furthermore, a weighted triplet loss is proposed to avoid trivial solutions and poor generalization. Extensive experimental results on standard datasets show that the proposed DeepQuan model outperforms the state-of-the-art unsupervised hashing methods for image retrieval tasks.

Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)9780999241127
Publication statusPublished - Jul 2018
Event27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Conference27th International Joint Conference on Artificial Intelligence, IJCAI 2018
Internet address

Scopus Subject Areas

  • Artificial Intelligence


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