Near-Duplicate Video Retrieval Through Toeplitz Kernel Partial Least Squares

  • Jia-Li Tao
  • Jian-Ming Zhang
  • Liang-Jun Wang
  • Xiang-Jun Shen
  • Zheng-Jun Zha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)


The existence of huge volumes of near-duplicate videos shows a rising demand on effective near-duplicate video retrieval technique in copyright violation and search result re-ranking. In this paper, Kernel Partial Least Squares (KPLS) is used to find strong information correlation in near-duplicate videos. Furthermore, to solve the problem of “curse of kernelization” when querying a large-scale video database, we propose a Toeplitz Kernel Partial Least Squares method. The Toeplitz matrix multiplication can be implemented by the Fast Fourier Transform (FFT) to accelerate the computation. Extensive experiments on the widely used CC_WEB_VIDEO dataset demonstrate that the proposed approach exhibits superior performance of near-duplicate video retrieval (NDVR) over state-of-the-art methods, such as BCS, SE, SSBelt and CCA, achieving a mean average precision (MAP) score of 0.9665.


Near-duplicate video Correlation-based retrieval KPLS FFT Toeplitz matrices 



This work was funded in part by the National Natural Science Foundation of China (No. 61572240, 61601202), Natural Science Foundation of Jiangsu Province (Grant No. BK20140571).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jia-Li Tao
    • 1
  • Jian-Ming Zhang
    • 1
  • Liang-Jun Wang
    • 1
  • Xiang-Jun Shen
    • 1
  • Zheng-Jun Zha
    • 2
  1. 1.School of Computer Science and Telecommunication EngineeringJiangsu UniversityZhenjiangChina
  2. 2.School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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