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Highly-Economized Multi-view Binary Compression for Scalable Image Clustering

  • Zheng Zhang
  • Li Liu
  • Jie Qin
  • Fan Zhu
  • Fumin Shen
  • Yong XuEmail author
  • Ling Shao
  • Heng Tao Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11216)

Abstract

How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations are learned by exploiting both sharable and individual information across multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective discrete optimization under \(\ell _{21}\)-norm constraint. By this means, heavy continuous-valued Euclidean distance computations can be successfully reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work specifically designed for scalable multi-view image clustering. Extensive experimental results on four large-scale image datasets show that HSIC consistently outperforms the state-of-the-art approaches, whilst significantly reducing computational time and memory footprint.

Keywords

Large-scale image clustering Binary code learning Binary clustering Multi-view features 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zheng Zhang
    • 1
    • 2
    • 3
  • Li Liu
    • 3
  • Jie Qin
    • 4
  • Fan Zhu
    • 3
  • Fumin Shen
    • 5
  • Yong Xu
    • 1
    Email author
  • Ling Shao
    • 3
  • Heng Tao Shen
    • 5
  1. 1.Harbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.The University of QueenslandBrisbaneAustralia
  3. 3.Inception Institute of Artificial IntelligenceAbu DhabiUAE
  4. 4.Computer Vision LaboratoryETH ZurichZürichSwitzerland
  5. 5.University of Electronic Science and Technology of ChinaChengduChina

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