Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 553-560.DOI: 10.11772/j.issn.1001-9081.2019101795

• CCF Bigdata 2019 • Previous Articles     Next Articles

Traffic image semantic retrieval method based on specific object self-recognition

Yi ZHAO1,2, Xing DUAN1(), Shiyi XIE1, Chunlin LIANG1,2   

  1. 1.College of Mathematics and Computer Science,Guangdong Ocean University,Zhanjiang Guangdong 524000,China
    2.Guangdong Zhanjiang Bay Laboratory,Fisheries Big Data Center of South China Sea,Zhanjiang Guangdong 524000,China
  • Received:2019-08-30 Revised:2019-10-24 Accepted:2019-10-24 Online:2019-10-31 Published:2020-02-10
  • Contact: Xing DUAN
  • About author:ZHAO Yi, born in 1984, Ph. D., lecturer. His research interests include software engineering, artificial intelligence.
    XIE Shiyi, born in 1963, M. S., professor. His research interests include software engineering.
    LIANG Chunlin, born in 1975, M. S., associate professor. His research interests include software engineering.
  • Supported by:
    the Guangdong Laboratory Autonomous Set Up Major Project for Southern Marine Science and Engineering(ZJW-2019-08);the Major Scientific Research Projects of Innovative and Strong University of Guangdong Ocean University(GDOU2017052501)


赵一1,2, 段兴1(), 谢仕义1, 梁春林1,2   

  1. 1.广东海洋大学 数学与计算机学院,广东 湛江 524000
    2.湛江湾实验室 南海渔业大数据中心,广东 湛江 524000
  • 通讯作者: 段兴
  • 作者简介:赵一(1984—),男,湖北武汉人,讲师,博士,CCF会员,主要研究方向:软件工程、人工智能
  • 基金资助:


In order to retrieve images of traffic violations from a large number of road traffic images, a semantic retrieval method based on specific object self-recognition was proposed. Firstly, road traffic domain ontology as well as road traffic rule description were established by experts in traffic domain. Secondly, traffic image features were extracted by Convolutional Neural Network (CNN), and combined with the strategy for classifying image features which is based on the proposed improved Support Vector Machine based Decision Tree (SVM-DT) algorithm, the specific objects and the spatial positional relationship between the objects in the traffic images were automatically recognized and mapped into the association relationship (rule instance) between the corresponding ontology instance and its objects. Finally, the image semantic retrieval result was obtained by reasoning based on ontology instances and rule instances. Experimental results show that the proposed method has higher accuracy, recall and retrieval efficiency compared to keyword and ontology traffic image semantic retrieval methods.

Key words: traffic ontology, image semantic retrieval, semantic reasoning, Support Vector Machine Decision Tree (SVM-DT) classification, object recognition



关键词: 交通领域本体, 图像语义检索, 语义推理, 支持向量机决策树分类, 目标识别

CLC Number: