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Guangnan Ye, Dong Liu, Jun Wang, Shih-Fu Chang. Large-Scale Video Hashing via Structure Learning. In International Conference on Computer Vision (ICCV), 2013.

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Recently, learning based hashing methods have become popular for indexing large-scale media data. Hashing methods map high-dimensional features to compact binary codes that are efficient to match and robust in preserving original similarity. However, most of the existing hashing methods treat videos as a simple aggregation of independent frames and index each video through combining the indexes of frames. The structure information of videos, e.g., discriminative local visual commonality and temporal consistency, is often neglected in the design of hash functions. In this paper, we propose a supervised method that explores the structure learning techniques to design efficient hash functions. The proposed video hashing method formulates a minimization problem over a structure-regularized empirical loss. In particular, the structure regularization exploits the common local visual patterns occurring in video frames that are associated with the same semantic class, and simultaneously preserves the temporal consistency over successive frames from the same video. We show that the minimization objective can be efficiently solved by an Accelerated Proximal Gradient (APG) method. Extensive experiments on two large video benchmark datasets (up to around $150K$ video clips with over $12$ million frames) show that the proposed method significantly outperforms the state-of-the-art hashing methods


Guangnan Ye
Dong Liu
Jun Wang
Shih-Fu Chang

BibTex Reference

   Author = {Ye, Guangnan and Liu, Dong and Wang, Jun and Chang, Shih-Fu},
   Title = {Large-Scale Video Hashing via Structure Learning},
   BookTitle = {International Conference on Computer Vision (ICCV)},
   Year = {2013}

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