Unsupervised video hashing by exploiting spatio-temporal feature

Chao Ma, Yun Gu, Wei Liu, Jie Yang, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


Video hashing is a common solution for content-based video retrieval by encoding high-dimensional feature vectors into short binary codes. Videos not only have spatial structure inside each frame but also have temporal correlation structure between frames, while the latter has been largely neglected by many existing methods. Therefore, in this paper we propose to perform video hashing by incorporating the temporal structure as well as the conventional spatial structure. Specifically, the spatial features of videos are obtained by utilizing Convolutional Neural Network (CNN), and the temporal features are established via Long-Short Term Memory (LSTM). The proposed spatio-temporal feature learning framework can be applied to many existing unsupervised hashing methods such as Iterative Quantization (ITQ), Spectral Hashing (SH), and others. Experimental results on the UCF-101 dataset indicate that by simultaneously employing the temporal features and spatial features, our hashing method is able to significantly improve the performance of existing methods which only deploy the spatial feature.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsAkira Hirose, Minho Lee, Derong Liu, Kenji Doya, Kazushi Ikeda, Seiichi Ozawa
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319466743
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9949 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Spatio-temporal feature
  • Unsupervised method
  • Video hashing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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