文献类型：期刊文献

中文题名：面向特定目标自识别的交通图像语义检索方法

英文题名：Traffic image semantic retrieval method based on specific object self-recognition

作者：赵一[1,2];段兴[1];谢仕义[1];梁春林[1,2]

机构：[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

年份：2020

卷号：40

期号：2

起止页码：553

中文期刊名：计算机应用

外文期刊名：Journal of Computer Applications

收录：CSTPCD、、北大核心2017、CSCD_E2019_2020、北大核心、CSCD

基金：南方海洋科学与工程广东省实验室自主立项重大项目(ZJW-2019-08);广东海洋大学创新强校重大科研项目(GDOU2017052501)~~

语种：中文

中文关键词：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

外文关键词：traffic ontology;image semantic retrieval;semantic reasoning;Support Vector Machine Decision Tree(SVM-DT)classification;object recognition

中文摘要：为了从海量的道路交通图像中检索出违反交通法规的图像,提出了一种特定目标自识别的语义图像检索方法。首先,通过交通领域专家建立交通领域本体及道路交通规则描述;然后,通过卷积神经网络(CNN)对交通图像的特征进行提取,并结合改进的支持向量机决策树(SVM-DT)算法对图像特征进行分类的策略,对交通图像中的特定目标及目标间空间位置关系进行自动识别,并映射成为相应的本体实例及其对象之间的关联关系(规则实例);最后,利用本体实例和规则实例,通过推理得到语义检索结果。实验结果表明,相比关键字和本体交通图像语义检索方法,所提方法具有更高的准确率、召回率和检索效率。

外文摘要：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.