Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  百年期刊
Journal of Tsinghua University(Science and Technology)    2016, Vol. 56 Issue (11) : 1137-1142     DOI: 10.16511/j.cnki.qhdxxb.2016.26.001
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
Download: PDF(3539 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
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.
Keywords attributed object detection      object detection      natural language processing     
ZTFLH:  TN911.73  
Issue Date: 26 November 2016
E-mail this article
E-mail Alert
Articles by authors
WANG Shengjin
Cite this article:   
ZHANG Xu,WANG Shengjin. Attributed object detection based on natural language processing[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(11): 1137-1142.
URL:     OR
[1] 杨德亮, 谢旭东, 李春文, 等. 基于分布式视频网络的交叉口车辆精确定位方法[J]. 清华大学学报(自然科学版), 2016, 56(3):281-286,293. YANG Deliang, XIE Xudong, Li Chunwen, et al. Accurate vehicle location method at an intersection based on distributed video networks[J]. J Tsinghua Univ (Sci and Tech), 2016, 56(3):281-286,293. (in Chinese)
[2] Zhang X, He F, Tian L, et al. Cognitive pedestrian detector:Adapting detector to specific scene by transferring attributes[J]. Neurocomputing, 2015, 149:800-810.
[3] Borth D, Ji R, Chen T, et al. Large-scale visual sentiment ontology and detectors using adjective noun pairs[C]//Proceedings of ACM MM. Barcelona, Spain:ACM, 2013:223-232.
[4] Chen T, Yu F X, Chen J, et al. Object-based visual sentiment concept analysis and application[C]//Proceedings of ACM MM. Orlando, USA:ACM, 2014:367-376.
[5] Jou B, Chen T, Pappas N, et al. Visual affect around the world:A large-scale multilingual visual sentiment ontology[C]//Proceedings of ACM MM. Brisbane, Australia:ACM, 2015:159-168.
[6] Wang X, Jia J, Tang J, et al. Modeling emotion influence in image social networks[J]. Affective Computing, IEEE Transactions on, 2015, 6(3):286-297.
[7] Duan K, Parikh D, Crandall D, et al. Discovering localized attributes for fine-grained recognition[C]//Proceedings of CVPR. Providence, USA:IEEE, 2012:3474-3481.
[8] Branson S, Van Horn G, Wah C, et al. The ignorant led by the blind:A hybrid human-machine vision system for fine-grained categorization[J]. International Journal of Computer Vision, 2014, 108(1-2):3-29.
[9] Hoffman J, Guadarrama S, Tzeng E S, et al. LSDA:Large scale detection through adaptation[C]//Advances in Neural Information Processing Systems. Montréal, Canada:MIT Press, 2014:3536-3544.
[10] Tommasi T, Patricia N, Caputo B, et al. A deeper look at dataset bias[J]. Pattern Recognition, 2015:504-516.
[11] Schmidhuber J. Deep learning in neural networks:An overview[J]. Neural Networks, 2015, 61:85-117.
[12] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of CVPR. Columbus, USA:IEEE, 2014:580-587.
[13] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2015, 37(9):1904-1916.
[14] Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2010, 32(9):1627-1645.
[15] Li Y, Wang S, Tian Q, et al. Feature representation for statistical-learning-based object detection:A review[J]. Pattern Recognition, 2015, 48(11):3542-3559.
[16] Cortes C, Vapnik V. Support vector machine[J]. Machine learning, 199
[1] LU Zhaolin, LI Shengbo, Schroeder Felix, ZHOU Jichen, CHENG Bo. Driving comfort evaluation of passenger vehicles with natural language processing and improved AHP[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(2): 137-143.
Full text



Copyright © Journal of Tsinghua University(Science and Technology), All Rights Reserved.
Powered by Beijing Magtech Co. Ltd