面向Logo识别的合成数据生成方法研究

江玉朝; 吉立新; 高超; 李邵梅

doi:10.11959/j.issn.2096-109x.2018043

您当前的位置：

首页 >

文章列表页 >

面向Logo识别的合成数据生成方法研究

学术论文 | 更新时间：2024-06-03

- 面向Logo识别的合成数据生成方法研究
- Research on synthesis data generation method for logo recognition
- 网络与信息安全学报 2018年4卷第5期页码：21-31
- 作者机构：
- 作者简介：
  
  [ "江玉朝（1994-），江苏盐城人，信息工程大学硕士生，主要研究方向为计算机视觉。" ]
  [ "吉立新（1969-），江苏淮安人，博士，信息工程大学研究员，主要研究方向为通信与信息系统。" ]
  [ "高超（1982-），河南郑州人，博士，信息工程大学助理研究员，主要研究方向为计算机视觉。" ]
  [ "李邵梅（1982-），湖北钟祥人，博士，信息工程大学副研究员，主要研究方向为计算机视觉。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61601513)
- DOI：10.11959/j.issn.2096-109x.2018043
  中图分类号： TP391
- 网络首发：2018-05，
  
  纸质出版：2018-05-15
- 稿件说明：
移动端阅览
江玉朝, 吉立新, 高超, 等. 面向Logo识别的合成数据生成方法研究[J]. 网络与信息安全学报, 2018,4(5):21-31.

Yuchao JIANG, Lixin JI, Chao GAO, et al. Research on synthesis data generation method for logo recognition[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 21-31.
江玉朝, 吉立新, 高超, 等. 面向Logo识别的合成数据生成方法研究[J]. 网络与信息安全学报, 2018,4(5):21-31. DOI： 10.11959/j.issn.2096-109x.2018043.

Yuchao JIANG, Lixin JI, Chao GAO, et al. Research on synthesis data generation method for logo recognition[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 21-31. DOI： 10.11959/j.issn.2096-109x.2018043.

摘要

针对深度学习框架下Logo识别任务中可训练样本稀疏的问题，提出了一种基于上下文的Logo数据合成算法，该算法综合利用了Logo对象内部、Logo周围邻域、Logo与其他对象之间以及Logo所处场景等多种类型的上下文信息指导Logo图像的合成。在FlickrLogos-32数据集上的实验结果显示，所提算法能够在不依赖额外手工标注的前提下，提升Logo识别算法的性能（mAP提升8.5%），验证了该合成算法的有效性。

Abstract

Aiming at the problem of training sample sparse in Logo recognition task under the deep learning framework

a Logo data synthesis algorithm based on contexts was proposed.The algorithm comprehensively utilizes various types of context information to guide the synthesis of Logo images

such as the interior of Logo object

the neighborhood of Logo object

the link between Logo object and other objects and the scene where Logo object lives in.The experimental results on the FlickrLogos-32 dataset show that the proposed algorithm can improve the performance of the Logo identification algorithm (mAP increase by 8.5%) without relying on additional manual annotation

verifying the effectiveness of the synthesis algorithm.

关键词

Keywords

references

符亚彬 . 基于 Logo 标志检测的暴恐视频识别系统的设计与实现 [D ] . 北京:北京交通大学 , 2016 .

FU Y B . Design and implementation of violence and fear video recognition system based on Logo mark detection [D ] . Beijing:Beijing Jiaotong University , 2016 .

GAO Y , WANG F , LUAN H , et al . Brand data gathering from live social media streams [C ] // ACM International Conference on Multimedia Retrieval . 2014 :169.

PAN C , YAN Z , XU X , et al . Vehicle logo recognition based on deep learning architecture in video surveillance for intelligent traffic system [C ] // IET International Conference on Smart and Sustainable City . 2013 : 123 - 126 .

HE K , GKIOXARI G , DOLLAR P , et al . Mask R-CNN [C ] // IEEE International Conference on Computer Vision . 2017 : 2980 - 2988 .

WANG X , SHRIVASTAVA A , GUPTA A . A-Fast-RCNN:hard positive generation via adversary for object detection [C ] // IEEE Conference on Computer Vision and Pattern Recognition . 2017 : 3039 - 3048 .

LIU W , ANGUELOV D , ERHAN D , et al . SSD:single shot MultiBox detector [M ] // Computer Vision-ECCV 2016 . Springer International Publishing , 2016 : 21 - 37 .

JOLY A , BUISSON O . Logo retrieval with a contrario visual query expansion [C ] // International Conference on Multimedia 2009 . 2009 : 581 - 584 .

KALANTIDIS Y , PUEYO L G , TREVISIOL M , et al . Scalable triangulation-based logo recognition [C ] // ACM International Conference on Multimedia Retrieval . 2011 : 1 - 7 .

ROMBERG S , PUEYO L G , LIENHART R , et al . Scalable logo recognition in real-world images [C ] // ACM International Conference on Multimedia Retrieval . 2011 :25.

HOI S C H , WU X , LIU H , et al . LOGO-Net:Large-scale deep logo detection and brand recognition with deep region-based convolutional networks [J ] . IEEE Transactions on Pattern Analysis ＆Machine Intelligence , 2015 , 46 ( 5 ): 2403 - 2412 .

BIANCO S , BUZZELLI M , MAZZINI D , et al . Deep learning for logo recognition [J ] . Neuro Computing , 2017 , 245 ( C ): 23 - 30 .

SU H , ZHU X , GONG S . Deep learning logo detection with data expansion by synthesising context [C ] // IEEE Winter Conference on Applications of Computer Vision . 2017 : 530 - 539 .

CHEN X , GUPTA A . Webly supervised learning of convolutional networks [C ] // IEEE International Conference on Computer Vision . 2016 : 1431 - 1439 .

SHRIVASTAVA A , GUPTA A , GIRSHICK R . Training region-based object detectors with online hard example mining [C ] // IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) . 2016 : 761 - 769 .

GUPTA A , VEDALDI A , ZISSERMAN A . Synthetic data for text localisation in natural images [C ] // IEEE Computer Vision and Pattern Recognition . 2016 : 2315 - 2324 .

JADERBERG M , SIMONYAN K , VEDALDI A , et al . Reading text in the wild with convolutional neural networks [J ] . International Journal of Computer Vision , 2016 , 116 ( 1 ): 1 - 20 .

GEORGAKIS G , MOUSAVIAN A , BERG A C , et al . Synthesizing training data for object detection in indoor scenes [C ] // Robotics:Science and Systems . 2017 .

EGGERT C , WINSCHEL A , LIENHART R . On the benefit of synthetic data for company logo detection [C ] // ACM International Conference on Multimedia . 2015 : 1283 - 1286 .

REN S , HE K , GIRSHICK R , et al . Faster R-CNN:towards real-time object detection with region proposal networks [C ] // International Conference on Neural Information Processing Systems . 2015 : 91 - 99 .

BENGIO Y , COLLOBERT R , WESTON J . Curriculum learning [C ] // ACM International Conference on Machine Learning . 2009 : 41 - 48 .

LIU B . Modest proposal for the principle of logo design [J ] . Packaging Engineering , 2005 , 127 ( 2 ): 222 - 222 .

OLIVA A , TORRALBA A . The role of context in object recognition [J ] . Trends in Cognitive Sciences , 2007 , 11 ( 12 ): 520 .

MOTTAGHI R , CHEN X , LIU X , et al . The role of context for object detection and semantic segmentation in the wild [C ] // IEEE Computer Vision and Pattern Recognition . 2014 : 891 - 898 .

KATTI H , PEELEN M V , ARUN S P . How do targets,nontargets,and scene context influence real-world object detection? [J ] . Attention Perception ＆ Psychophysics , 2017 ( 2 ): 1 - 16 .

ZHOU B , LAPEDRIZA A , KHOSLA A , et al . Places:a 10 million image database for scene recognition [J ] . IEEE Trans Pattern Anal Mach Intell , 2017 , 99 : 1 - 1 .

GUO J , GOULD S . Deep CNN ensemble with data augmentation for object detection [J ] . Computer Science , 2015 .

OLIVEIRA G , FRAZÃO X , PIMENTEL A , et al . Automatic graphic logo detection via fast region-based convolutional networks [C ] // IEEE International Joint Conference on Neural Networks . 2016 .

MUNNEKE J , BRENTARI V , PEELEN M . The influence of scene context on object recognition is independent of attentional focus [J ] . Frontiers in Psychology , 2013 , 4 ( 8 ): 552 .

NGUYEN H V , HO H T , PATEL V M , et al . DASH-N:joint hierarchical domain adaptation and feature learning [J ] . IEEE Transactions on Image Processing , 2015 , 24 ( 12 ): 5479 - 5491 .

浏览量

1837

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

DynaSynth：面向非结构化网络安全语料的大语言模型微调数据动态生成代理工具

基于双路特征提取和自适应融合的钓鱼网站检测

DNS异常检测：大规模实验分析

FL-IoV：基于联邦学习的综合车联网入侵检测方法

基于分层结构的多任务对抗样本归因方法