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清华大学学报(自然科学版)  2016, Vol. 56 Issue (11): 1137-1142    DOI: 10.16511/j.cnki.qhdxxb.2016.26.001
  电子工程 本期目录 | 过刊浏览 | 高级检索 |
基于自然语言处理的特定属性物体检测
张旭, 王生进
清华大学 电子工程系, 智能技术与系统国家重点实验室, 信息技术国家实验室, 北京 100084
Attributed object detection based on natural language processing
ZHANG Xu, WANG Shengjin
State Key Laboratory of Intelligent Technology and System, National Laboratory for Information Science and Technology, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
全文: PDF(3539 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 该文研究如何在图片中定位特定属性物体(如“废弃的车”等)。由于一个物体可能包含几十甚至上百个非互斥的属性,训练特定属性物体检测器的难点是为大量的特定属性物体收集训练图片并标定边界框。该文提出使用特定属性物体分类器扩展物体检测器获取特定属性物体检测器的方法。其中的特定属性物体分类器通过使用从互联网上挖掘的图片以及从物体检测器和自然语言处理工具获取的标注信息训练得到。构建了特定属性物体检测数据库并对特定属性物体检测器的性能进行分析,结果表明:特定属性检测器的平均精度均值比物体检测器相对提高30%。
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张旭
王生进
关键词 特定属性物体检测物体检测自然语言处理    
Abstract:This paper addresses the problem of localizing an attributed object, such as "abandoned car", in images. Since one object may have tens or even hundreds of non-exclusive attributes, the main difficulties of attributed object detection are manually collecting training images and labeling the bounding boxes for a large number of attributed objects. This attributed object detector extends the object detector with an attributed object classifier. The attributed object classifier is trained by images from the Internet and labeling information gathered by the object detector and a natural language processing tool. An attributed object detection dataset was developed to evaluate the attributed object detectors. Tests show that this attributed object detector has good performance gains of 30% for the mean average precision compared to generic object detectors.
Key wordsattributed object detection    object detection    natural language processing
收稿日期: 2016-06-02      出版日期: 2016-11-26
ZTFLH:  TN911.73  
通讯作者: 王生进,教授,E-mail:wgsgj@tsinghua.edu.cn     E-mail: wgsgj@tsinghua.edu.cn
引用本文:   
张旭, 王生进. 基于自然语言处理的特定属性物体检测[J]. 清华大学学报(自然科学版), 2016, 56(11): 1137-1142.
ZHANG Xu, WANG Shengjin. Attributed object detection based on natural language processing. Journal of Tsinghua University(Science and Technology), 2016, 56(11): 1137-1142.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.26.001  或          http://jst.tsinghuajournals.com/CN/Y2016/V56/I11/1137
  图1 特定属性物体检测器结构图
  图2 特定属性物体检测器以及物体检测器在“废弃的车”测试集上的检测结果(虚线框为真值,实线框为正确的检测结果,点线框为错误的检测结果)
  图3 特定属性物体分类器训练流程图
  图4 布偶猫的检测结果(虚线框为真值,实线框为正确的检测结果,点线框为错误的检测结果)
  图5 惠比特犬的检测结果(虚线框为真值,实线框为正确的检测结果,点线框为错误的检测结果)
  图6 特定属性物体检测器的性能
  表1 不同检测器对各物体的检测性能(mAP)
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