Laser & Optoelectronics Progress, Volume. 57, Issue 22, 221012(2020)

Microvideo Multilabel Learning Model Based on Multiview Low-Rank Representation

Wei Lü1, Desheng Li1、*, Lang Tan2, Peiguang Jing1, and Yuting Su1
Author Affiliations
  • 1School of Electronics and Information Engineering, Tianjin University, Tianjin 300072, China
  • 2Beijing Smartchip Microelectronics Technology Co., Ltd., Beijing 102200, China
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    Figures & Tables(7)
    Illustration of proposed model
    Sample video with different labels selected from dataset
    Convergence verification graphs. (a) Variation of Zdiff with model iteration; (b) variation of average precision with model iteration
    Effect of different parameters on average precision. (a) Effect of λ2 on average precision; (b) effect of λ4 on average precision
    Label correlation matrix comparison. (a) Normalized correlation matrix for true label; (b) normalized correlation matrix S˙after the iteration
    • Table 1. Ablation experiment results

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      Table 1. Ablation experiment results

      Evaluation metricsNo LRNo LFNo LC
      Average precision difference-0.0508-0.2423-0.0172
      Hamming loss difference0.00130.00290.0005
      Ranking loss difference0.00790.05210.0049
      Coverage difference0.34924.69140.2851
      One-error difference0.01750.14820.0230
    • Table 2. Performance comparison of different algorithms

      View table

      Table 2. Performance comparison of different algorithms

      MethodAverage precisionHamming lossRanking lossCoverageOne-error
      DNMF0.4673±0.00630.0154±0.00010.1077±0.00828.3853±0.16210.6487±0.0082
      LRR0.5489±0.00570.0154±0.00010.0991±0.00518.4056±0.18030.3039±0.0057
      GLOCAL0.7527±0.00640.0133±0.00200.0515±0.00153.9943±0.10560.2457±0.0032
      MLKNN0.7843±0.00530.0134±0.00010.0476±0.00584.0204±0.18740.3087±0.0058
      Googlenet0.6676±0.00440.0176±0.00020.4349±0.00664.5680±0.06000.4349±0.0066
      C3D0.7149±0.00890.0146±0.00030.3694±0.00283.9041±0.20330.3694±0.0088
      C2AE0.8013±0.00220.0128±0.00010.0481±0.00413.6942±0.14710.2381±0.0026
      Proposed0.8055±0.00280.0128±0.00010.0432±0.00233.6732±0.12740.2561±0.0065
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    Wei Lü, Desheng Li, Lang Tan, Peiguang Jing, Yuting Su. Microvideo Multilabel Learning Model Based on Multiview Low-Rank Representation[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221012

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    Paper Information

    Category: Image Processing

    Received: Mar. 10, 2020

    Accepted: Apr. 10, 2020

    Published Online: Nov. 12, 2020

    The Author Email: Desheng Li (lidesheng1996@tju.edu.cn)

    DOI:10.3788/LOP57.221012

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