铁道标准设计2018，Vol.62Issue(6)：30-33,4.DOI:10.13238/j.issn.1004-2954.201707190002
基于近义词分配的铁路扣件状态检测
Railway Fastener State Inspection Based on Homoionym-assignment
摘要
Abstract
Aiming at the problem of large quantization error in traditional Bag of words (BOW) model when performing the low-level feature coding,a model of railway fastener detection based on homoionym-assignment is proposed.Firstly,the Latent Dirichlet Allocation (LDA) model is used to analyze and obtain the latent topic distribution induced by the visual words.Secondly,the relative entropy is introduced to measure semantic distance between visual words and obtain semantically related words.And then,soft-assignment is adopted to realize the mapping of low-level features on some homoionym.Finally,the Support Vector Machine (SVM) is applied to fulfill fastener inspection.The experiment on four types of fasteners shows that the proposed model can improve effectively the accuracy of fastener classification.关键词
铁路扣件/检测/词包模型/相对熵/潜在狄利克雷分布/近义词/视觉单词Key words
Railway fastener/Inspection/Bag-of-Words (BOW) model/Relative entropy/Latent Dirichlet Allocation (LDA)/Homoionym/Visual word分类
交通工程引用本文复制引用
李爽,李柏林..基于近义词分配的铁路扣件状态检测[J].铁道标准设计,2018,62(6):30-33,4.基金项目
四川省科技支撑计划项目(2016GZ0194) （2016GZ0194）
