Multi-level supervised hashing with deep features for efficient image retrieval

Wing W.Y. NG, Jiayong LI, Xing TIAN, Hui WANG, Sam KWONG, Jonathan WALLACE

Research output: Journal PublicationsJournal Article (refereed)peer-review

17 Citations (Scopus)


Image hashing based on deep convolutional neural networks (CNN), deep hashing, has acquired breakthrough in image retrieval. Although deep features from various CNN layers have various levels of information, most of the existing deep hashing methods extract the feature vector only from the output of the penultimate fully-connected layer, focusing primarily on semantic information whilst ignoring detailed structure information. This calls for research on multi-level hashing, utilizing multi-level features to exploit different levels of CNN characteristics. To fill this gap, a novel image hashing method, Multi-Level Supervised Hashing with deep feature (MLSH), is proposed in this paper to further exploit multiple levels of deep image features. It uses a multiple-hash-table mechanism to integrate multi-level features extracted from an individual deep convolutional neural network. It takes advantage of the complementarity among multi-level features from various layers of a single deep network. High-level features reveal the semantic content of the image, while low-level features provide the structural information that is missing in high-level features. Instead of simple concatenation, several hash tables are trained individually using different levels of features from different layers, which are then integrated for efficient image retrieval. The method has been systematically evaluated through experiments on three image databases, including CIFAR-10, MNIST and NUSWIDE, and has thus been demonstrated to set a new state of the art in image hashing, outperforming several state-of-the-art hashing methods. Furthermore, the recall and precision can be balanced and improved simultaneously.
Original languageEnglish
Pages (from-to)171-182
Early online date14 Feb 2020
Publication statusPublished - 25 Jul 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61876066, Grant 61572201, Grant 61772344, and Grant 61672443, in part by the Guangzhou Science and Technology Plan Project under Grant 201804010245, and EU Horizon 2020 Programme (700381, ASGARD), and in part by the Hong Kong RGC General Research Funds under Grant 9042489 (CityU 11206317), Grant 9042816 (CityU 11209819) and Grant 9042322 (CityU 11200116).


  • Image retrieval
  • Multi-level deep feature
  • Multi-table mechanism
  • Structural and semantic similarity


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