Home > Papers

 
 
Unsupervised Video Hashing by Exploiting Spatio-Temporal Feature
Chao Ma 1, Yun Gu 1, Wei Liu 1, Jie Yang 1, Xiangjian He 2
1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240.
2. University of Technology, Sydney, Australia.
*Correspondence author
#Submitted by
Subject:
Funding: Ph.D. Programs Foundation of Ministry of Education of China （No.No.20120073110018）
Opened online:13 June 2016
Accepted by: none
Citation: Chao Ma, Yun Gu, Wei Liu.Unsupervised Video Hashing by Exploiting Spatio-Temporal Feature[OL]. [13 June 2016] http://en.paper.edu.cn/en_releasepaper/content/4691838
 
 
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.
Keywords:Video Hashing, Unsupervised Method, Spatio-temporal Feature
 
 
 

For this paper
  • ● PDF (0B)
  • ● Revision 0 　　
  • ● Print this paper
  • ● Recommend this paper to a friend
  • ● Add to my favorite list

    Saved Papers

    Please enter a name for this paper to be shown in your personalized Saved Papers list

Tags
Add yours

Related Papers

Statistics
PDF Downloaded 45
Bookmarked 0
Recommend 0
Comments Array
Submit your papers