Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (3): 888-893.DOI: 10.11772/j.issn.1001-9081.2018081767

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Railway fastener classification model based on sLDA combined with global and local constraints

YANG Fei, LUO Jianqiao, LI Bailin   

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu Sichuan 610031, China
  • Received:2018-08-24 Revised:2018-10-10 Online:2019-03-10 Published:2019-03-11
  • Supported by:
    This work is partially supported by the Science and Technology Support Project of Sichuan Province (2018GZ0361).


杨飞, 罗建桥, 李柏林   

  1. 西南交通大学 机械工程学院, 成都 610031
  • 通讯作者: 李柏林
  • 作者简介:杨飞(1994-),男,重庆垫江人,硕士研究生,主要研究方向:机器视觉、图像处理、模式识别;罗建桥(1991-),男,湖南湘潭人,博士研究生,主要研究方向:图像语义分析、机器学习;李柏林(1962-),男,广西桂林人,教授,博士生导师,博士,主要研究方向:图像处理、机器视觉。
  • 基金资助:

Abstract: Aiming at the ignorance of target structure in test topic distribution due to the lack of annotation in supervised Latent Dirichlet Allocation (sLDA) model, a sLDA fastener image classification model combined with global and local constraints (glc-LDA) was proposed. Firstly, the training images were manually labeled, and the training topic distribution with structural information was learned in sLDA model. Then, the test topic distribution was calculated to obtain the image category probabilities as global constraints, the topic similarities of training sub-blocks and test sub-blocks as local constraints. Finally, updated test topic distribution was obtained by weighted summation of training topic distribution with the product of global and local constraints as updated weights. The image category labels were obtained in Softmax classifier by the updated topics. The experimental results show that the proposed algorithm can express the structural information of fastener and compared with sLDA model, the distinction of each category of fastener images is enhanced, and the false detection rate is reduced by 55%.

Key words: railway fastener classification, supervised Latent Dirichlet Allocation (sLDA), topic model, annotation of word, target structure, update topic distribution

摘要: 针对监督潜在狄利克雷分布(sLDA)模型中测试图像缺乏标注,导致测试主题分布忽略目标结构的问题,提出一种结合全局和局部约束的sLDA(glc-sLDA)扣件图像分类模型。首先,人工标注训练图像,并在sLDA模型中学习得到含有结构信息的训练主题分布;然后,计算测试主题分布,将测试图像的类别概率作为全局约束,将测试图像子块与训练图像子块的主题相似程度作为局部约束;最后,以全局和局部约束的乘积为更新权值,对训练主题分布加权求和得到新的测试主题分布,并在Softmax分类器中得到测试图像的分类结果。实验结果表明,glc-sLDA模型能表达扣件结构信息,与sLDA相比,各类别的扣件图像区分性增强,分类误检率减小了55%。

关键词: 铁路扣件分类, 监督潜在狄利克雷分布, 主题模型, 单词标注, 目标结构, 更新主题分布

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