面向Logo识别的合成数据生成方法研究

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

Abstract

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.

Key words: Logo recognition, data synthesis, context, deep learning, data augmentation

CLC Number:

TP391

Yuchao JIANG,Lixin JI,Chao GAO,Shaomei LI. Research on synthesis data generation method for logo recognition[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 21-31.

Figures/Tables 14

					AP值
方法	训练集/测试集划分（每类图片数）	Adidas	Aldi	Apple	Becks	BMW	Carls	Chim	Coke	mAP
		Corona	DHL	Erdi	Esso	Fedex	Ferra	Ford	Fost
		Google	Guin	Hein	HP	Milka	Nvid	Paul	Pepsi
		Ritt	Shell	Sing	Starb	Stel	Texa	Tsin	Ups
		23.7	57.5	63.0	69.6	63.7	50.6	55.2	26.8
RealImg（[12]）	训练：10 真实	79.0	25.8	61.2	44.2	45.9	80.6	64.3	43.2	50.4%
	测试：60 真实	47.7	58.2	61.8	21.3	19.4	17.4	48.2	17.8
		34.8	45.8	71.8	70.2	79.6	56.7	56.9	52.2
		9.4	47.3	9.6	70.3	39.9	28.3	15.8	21.7
SynImg-32Cls	训练：100 合成	6.1	11.1	4.1	44.7	22.9	60.9	43.6	28.8	27.6%
（[12]）	测试：60 真实	23.0	16.7	43.1	9.9	4.6	1.1	39.1	9.7
		22.7	38.3	15.5	65.6	28.7	55.1	27.4	20.1
		26.8	63.7	65.8	72.7
SynImg-32Cls +	训练：100 合成	76.0	31.5	63.0	52.2				46.6	54.8%
RealImg（[12]）	精调：10 真实	58.0	52.6	65.2	23.2	24.0	12.5	54.1
	测试：60 真实	37.9		75.0		79.0	64.2	57.4	54.4
		24.4	57.2	66.6	72.0	70.8	42.8	55.3	24.8
RealImg（Ours）	训练：10 真实	82.8	29.5	62.5	44.1	42.7	87.2	59.3	39.9	50.5%
	测试：60 真实	51.2	54.6	65.1	24.2	15.5	16.9	52.3	17.4
		32.3	44.7	72.7	69.7	77.3	62.5	52.6	44.3
			54.0	34.6	44.1	17.6	18.4	33.9	20.6
SynImg-32Cls	训练：100 合成	8.2	21.5	12.1	49.6	16.6	28.2	35.4	50.3	32.6%
（Ours）	测试：60 真实	46.4	40.0	33.1	26.1	9.9	18.8	64.5	22.3
		39.4	45.5	15.8	55.5	17.6	47.5	38.7	39.9
		31.0				76.6	51.1	62.1	29.8
SynImg-32Cls+	训练：100 合成					49.0	87.9	76.9
RealImg（Ours）	精调：10 真实								22.8
	测试：60 真实		45.5		72.5
		36.2	61.3	30.1	46.9	25.3	30.8	32.2	22.3
SynImg-32Cls +	训练：100 合成 +10 真实	11.4	16.7	19.6	47.1	28.0	33.2	33.8	50.1	34.2%
RealImg（fusion）	（混合）	45.7	41.6	37.1	25.6	13.0	16.3	63.7	20.9
（Ours）	测试：60 真实	33.4	45.0	14.1	62.7	22.8	46.4	43.5	36.6
SynImg-32Cls +	训练：100 合成 +10 真实	31.1	71.2	71.6	71.7	84.4	48.0	66.3	28.5
RealImg（fusion）	（混合）	84.4	33.3	82.0	51.7	51.0	90.3	76.4	54.8	58.9%
+RealImg（Ours）	精调：10 真实	59.6	67.8	69.4	37.8	23.2	22.9	66.0	23.6
	测试：60 真实	40.6	46.0	75.3	77.2	83.5	67.8	63.6	64.2

References 29

[1]	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.
[2]	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.
[3]	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.
[4]	HE K , GKIOXARI G , DOLLAR P ,et al. Mask R-CNN[C]// IEEE International Conference on Computer Vision. 2017: 2980-2988.
[5]	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.
[6]	LIU W , ANGUELOV D , ERHAN D ,et al. SSD:single shot MultiBox detector[M]// Computer Vision-ECCV 2016. Springer International Publishing, 2016: 21-37.
[7]	JOLY A , BUISSON O . Logo retrieval with a contrario visual query expansion[C]// International Conference on Multimedia 2009. 2009: 581-584.
[8]	KALANTIDIS Y , PUEYO L G , TREVISIOL M ,et al. Scalable triangulation-based logo recognition[C]// ACM International Conference on Multimedia Retrieval. 2011: 1-7.
[9]	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.
[10]	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.
[11]	BIANCO S , BUZZELLI M , MAZZINI D ,et al. Deep learning for logo recognition[J]. Neuro Computing, 2017,245(C): 23-30.
[12]	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.
[13]	CHEN X , GUPTA A . Webly supervised learning of convolutional networks[C]// IEEE International Conference on Computer Vision. 2016: 1431-1439.
[14]	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.
[15]	GUPTA A , VEDALDI A , ZISSERMAN A . Synthetic data for text localisation in natural images[C]// IEEE Computer Vision and Pattern Recognition. 2016: 2315-2324.
[16]	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.
[17]	GEORGAKIS G , MOUSAVIAN A , BERG A C ,et al. Synthesizing training data for object detection in indoor scenes[C]// Robotics:Science and Systems. 2017.
[18]	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.
[19]	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.
[20]	BENGIO Y , COLLOBERT R , WESTON J . Curriculum learning[C]// ACM International Conference on Machine Learning. 2009: 41-48.
[21]	LIU B . Modest proposal for the principle of logo design[J]. Packaging Engineering, 2005,127(2): 222-222.
[22]	OLIVA A , TORRALBA A . The role of context in object recognition[J]. Trends in Cognitive Sciences, 2007,11(12): 520.
[23]	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.
[24]	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.
[25]	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.
[26]	GUO J , GOULD S . Deep CNN ensemble with data augmentation for object detection[J]. Computer Science, 2015.
[27]	OLIVEIRA G , FRAZ?O X , PIMENTEL A ,et al. Automatic graphic logo detection via fast region-based convolutional networks[C]// IEEE International Joint Conference on Neural Networks. 2016.
[28]	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.
[29]	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.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	Logo类别数	对象总数	图像总数	是否公开
BelgaLogos^[7]	37	2 695	1 951	是
FlickrLogos-27^[8]	27	4 671	1 080	是
FlickrLogos-32^[9]	32	3 404 注1	2 240	是
LOGO-NET^[10]	160	130 608	73 414	否
Logos-32plus^[11]	32	12 302	7 830	是
TopLogo10^[12]	10	863	700	是
注1 已与文献[12]的作者取得联系，证实文献[12]中关于FlickrLogos-32数据集对象数量的统计有误，正确数量为3 404。

				AP值
	Adidas	Aldi	Apple	Becks	BMW	Carls	Chim	Coke
方法	Corona	DHL	Erdi	Esso	Fedex	Ferra	Ford	Fost	mAP
	Google	Guin	Hein	HP	Milka	Nvid	Paul	Pepsi
	Ritt	Shell	Sing	Starb	Stel	Texa	Tsin	Ups
	31.0	69.0	74.3		76.6	51.1		29.8
SynImg-32Cls+		35.3	70.9	53.5	49.0		76.9	52.9
RealImg	61.7	66.9	69.3		25.8			22.8
（Our Baseline）	41.8	45.5		72.5	79.7	69.2	62.1
	32.8	69.7		77.6	76.8		61.8	29.1
Transparent Only+	88.0	35.9		52.9	43.9	87.5	76.3	47.8	57.8%
RealImg	59.3	61.5	68.3	33.2	24.8	24.3	67.4	18.5
		47.5	78.6	73.6	80.3	69.2	61.3	61.2
	32.7	66.7	73.8	75.5	77.4	48.3	59.7
Pixel-level Only+	87.5		71.7	53.6		87.7	72.2	53.5	58.1%
RealImg		65.9	71.3	29.7		25.4	62.9
	36.5		75.4		79.3	66.6	63.4	59.3
	28.5	65.2	68.2	74.5		46.6	60.0	30.8
Random Context+	86.4	33.5	70.1		50.4	83.5	75.9	52.2	56.7%
RealImg	61.6	63.3	69.8	28.5	26.3	22.5	62.6	23.8
	35.6	47.2	74.6	74.0	79.5		61.0	56.1
	30.5	63.7	72.0	73.3	73.4	49.0	57.2	27.1
No Logo Transform+	85.1	33.9	68.1	52.8	47.6	85.0	75.4	48.6	56.3%
RealImg	56.2	62.2	69.1	33.9	24.0	26.0	67.6	23.6
	34.0	46.3	73.5	72.9	80.5	69.0	61.8	58.4
			73.6	74.6	77.0	50.0	61.3	30.5
Random Position+	83.0	35.8	71.7	53.9	47.0	86.6			58.2%
RealImg	57.9			33.7	20.4	24.9	64.3	24.1
	41.7	47.5	77.3	74.4		68.4		60.2

Research on synthesis data generation method for logo recognition

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 29

Related Articles 15

Metrics

Recommended 0

[1]	Guanyun FENG, Cai FU, Jianqiang LYU, Lansheng HAN. Insider threat detection based on operational attention and data augmentation [J]. Chinese Journal of Network and Information Security, 2023, 9(3): 102-112.
[2]	Xiaomeng LI, Daidou GUO, Xunfang ZHUO, Heng YAO, Chuan QIN. Carrier-independent screen-shooting resistant watermarking based on information overlay superimposition [J]. Chinese Journal of Network and Information Security, 2023, 9(3): 135-149.
[3]	Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN. Encrypted traffic classification method based on convolutional neural network [J]. Chinese Journal of Network and Information Security, 2022, 8(6): 84-91.
[4]	Dengyong ZHANG, Huang WEN, Feng LI, Peng CAO, Lingyun XIANG, Gaobo YANG, Xiangling DING. Image inpainting forensics method based on dual branch network [J]. Chinese Journal of Network and Information Security, 2022, 8(6): 110-122.
[5]	Jiaying LIN, Wenbo ZHOU, Weiming ZHANG, Nenghai YU. Lip forgery detection via spatial-frequency domain combination [J]. Chinese Journal of Network and Information Security, 2022, 8(6): 146-155.
[6]	Jinyin CHEN, Changan WU, Haibin ZHENG. Novel defense based on softmax activation transformation [J]. Chinese Journal of Network and Information Security, 2022, 8(2): 48-63.
[7]	Baolin QIU, Ping YI. Adversarial examples defense method based on multi-dimensional feature maps knowledge distillation [J]. Chinese Journal of Network and Information Security, 2022, 8(2): 88-99.
[8]	Lijuan LI, Man LI, Hongjun BI, Huachun ZHOU. Multi-type low-rate DDoS attack detection method based on hybrid deep learning [J]. Chinese Journal of Network and Information Security, 2022, 8(1): 73-85.
[9]	Zhongyuan QIN, Zhaoxiang HE, Tao LI, Liquan CHEN. Adversarial example defense algorithm for MNIST based on image reconstruction [J]. Chinese Journal of Network and Information Security, 2022, 8(1): 86-94.
[10]	Deqing ZOU, Xiang LI, Minhuan HUANG, Xiang SONG, Hao LI, Weiming LI. Intelligent vulnerability detection system based on graph structured source code slice [J]. Chinese Journal of Network and Information Security, 2021, 7(5): 113-122.
[11]	Zhenglong WANG, Baowen ZHANG. Survey of generative adversarial network [J]. Chinese Journal of Network and Information Security, 2021, 7(4): 68-85.
[12]	Binglong LI, Jinlong TONG, Yu ZHANG, Yifeng SUN, Qingxian WANG, Chaowen CHANG. Auto forensic detecting algorithms of malicious code fragment based on TensorFlow [J]. Chinese Journal of Network and Information Security, 2021, 7(4): 154-163.
[13]	Qingyin TAN, Yingming ZENG, Ye HAN, Yijing LIU, Zheli LIU. Survey on backdoor attacks targeted on neural network [J]. Chinese Journal of Network and Information Security, 2021, 7(3): 46-58.
[14]	Luhui YANG,Huiwen BAI,Guangjie LIU,Yuewei DAI. Lightweight malicious domain name detection model based on separable convolution [J]. Chinese Journal of Network and Information Security, 2020, 6(6): 112-120.
[15]	Ximeng LIU,Lehui XIE,Yaopeng WANG,Xuru LI. Adversarial attacks and defenses in deep learning [J]. Chinese Journal of Network and Information Security, 2020, 6(5): 36-53.