基于深度哈希学习的商标图像检索研究

doi:10.3969/j.issn.1000-5641.2018.05.015

华东师范大学学报(自然科学版) ›› 2018, Vol. 2018 ›› Issue (5): 172-182.doi: 10.3969/j.issn.1000-5641.2018.05.015

基于深度哈希学习的商标图像检索研究

袁培森¹, 张勇², 李美玲¹, 顾兴健¹

1. 南京农业大学信息科学技术学院, 南京 210095;
2. 南京工程学院基础部, 南京 211167

收稿日期:2018-06-27 出版日期:2018-09-25 发布日期:2018-09-26
作者简介:袁培森,男,博士,讲师,主要从事数据挖掘、海量数据处理与分析研究.E-mail:peiseny@njau.edu.cn.
基金资助:
国家自然科学基金（61502236）；中央高校基本科研业务费专项资金资助（KYZ201752）

Research on trademark image retrieval based on deep Hashing

YUAN Pei-sen¹, ZHANG Yong², LI Mei-ling¹, GU Xing-jian¹

1. College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;
2. Department of Mathematics and Physics, Nanjing Institute of Technology, Nanjing 211167, China

Received:2018-06-27 Online:2018-09-25 Published:2018-09-26

摘要/Abstract

摘要： 大规模图像检索具有广泛的应用前景，其核心在于图像特征提取和高效相似性计算.深度学习技术在图像特征提取具有较强的特征表示能力，同时哈希技术在高维数据近似计算方面具有较好的性能.目前，基于哈希学习的技术在大规模图像检索及相似性查询方面获得了广泛的研究和应用.本文结合卷积神经网络和哈希技术实现商标图像检索，通过深度学习技术提取商标图像特征，使用位哈希对数据对象编码，在海明空间折中查询的质量和效率.基于卷积神经网络模型，提出了深度哈希算法，并研究了损失函数和该数据集上的优化器选择，通过获取符合哈希编码规范的位编码实现对在二元空间对商标图像数据快速检索，该方法分为离线深度哈希学习和在线查询两个阶段.在真实商标数据集上进行实验，实验结果表明，本文方法能够在商标数据集上获得较高质量的位编码，并具有较高的检索精确度和在线查询效率.

关键词: 深度学习, 哈希学习, 商标检索, 卷积神经网络, 位编码

Abstract: Large-scale image retrieval has great potential for a vast number of applications. The fundamentals of image retrieval lie in image feature extraction and high-efficiency similarity evaluation. Deep learning has great capability for feature representation in image objects, while the Hashing technique has better efficiency for high-dimensional data approximation queries. At present, hash learning technology has been widely researched and applied in large-scale image retrieval for the similarity query. This paper extracts trademark image features using convolutional neural network techniques; the data objects are then encoded with bit codes and an approximate query is applied in Hamming space with high efficiency. In this paper, the convolutional neural network is employed and a deep learning based Hash algorithm is proposed; in addition, the loss function and optimizer for the trademark dataset are studied. By obtaining the bit codes that satisfy the Hash coding criterion, the retrieval of trademark data is efficient. Our method can be divided into offline deep Hash learning and online query stages. Experiments are conducted on real trademark data sets, and the results show that our method can obtain high-quality bit code, which has high retrieval accuracy and online query efficiency.

Key words: deep learning, Hash learning, trademark image retrieval, convolutional neural networks, bit encoding

中图分类号:

TP391

袁培森, 张勇, 李美玲, 顾兴健. 基于深度哈希学习的商标图像检索研究[J]. 华东师范大学学报(自然科学版), 2018, 2018(5): 172-182.

YUAN Pei-sen, ZHANG Yong, LI Mei-ling, GU Xing-jian. Research on trademark image retrieval based on deep Hashing[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(5): 172-182.

参考文献

[1] 黄元元, 刘宁钟. 利用特征点平均矩特征的商标图像检索[J]. 中国图象图形学报. 2010, 15(4):637-644.
[2] 宋瑞霞, 孙红磊, 王小春, 等. 边界特征和区域特征相结合的商标检索算法[J]. 软件学报. 2012, 23(2):85-93.
[3] 张玲, 邹北骥, 孙家广, 等. 一种基于极坐标下分块的商标图像检索新方法[J]. 小型微型计算机系统. 2007, 28(1):66-69.
[4] TURSUN O, KALKAN S. METU dataset:A big dataset for benchmarking trademark retrieval[C]//14th IAPR International Conference on Machine Vision Applications. New York:IEEE, 2015:514-517.
[5] YAN Y, REN J, LI Y, et al. Adaptive fusion of color and spatial features for noise-robust retrieval of colored logo and trademark images[J]. Multidimensional Systems and Signal Processing. 2016, 27(4):1-24.
[6] ANUAR F M, SETCHI R, LAI Y K. Trademark image retrieval using an integrated shape descriptor[J]. Expert Systems with Applications. 2013, 40(1):105-121.
[7] 孙兴华, 郭丽. 基于子图像多特征组合的商标图像检索[J]. 模式识别与人工智能. 2002, 15(1):14-20.
[8] WANG J, ZHANG T, SONG J, et al. A survey on learning to hash[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4):769-790.
[9] DATAR M, IMMORLICA N, INDYK P, et al. Locality-sensitive hashing scheme based on p-stable distributions[C]//Twentieth Symposium on Computational Geometry. New York:ACM, Symposium On Computational Geometry, 2004:253-262.
[10] STRECHA C, BRONSTEIN A M, BRONSTEIN M M, et al. LDAHash:Improved matching with smaller descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(1):66-78.
[11] WEISS Y, TORRALBA A, FERGUS R, et al. Spectral hashing[C]//Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS). Vancouver:ACM.2008:1753-1760.
[12] WAN J, WANG D, HOI S C, et al. Deep learning for content-based image retrieval:A comprehensive study[C]//Acm Multimedia. New York:ACM, 2014:157-166.
[13] CRUZROA A, OVALLE J E, MADABHUSHI A, et al. A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection[C]//Medical Image Computing and Computer Assisted Intervention. Berlin:Springer Berlin Heidelberg. 2013:403-410.
[14] ZHU H, LONG M, WANG J, et al. Deep hashing network for efficient similarity retrieval[C]//National Conference on Artificial Intelligence. CA:AAAI, 2016:2415-2421.
[15] 彭天强, 粟芳. 基于深度卷积神经网络和二进制哈希学习的图像检索方法[J]. 电子与信息学报. 2016, 38(8):2068-2075.
[16] LIU H, WANG R, SHAN S, et al. Deep Supervised Hashing for Fast Image Retrieval[C]//IEEE Computer Society. IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE, 2016:2064-2072.
[17] XIA R, PAN Y, LAI H, et al. Supervised hashing for image retrieval via image representation learning[C]//National Conference On Artificial Intelligence, CA:AAAI, 2014:2156-2162.
[18] 龚震霆, 陈光喜, 任夏荔, 等. 基于卷积神经网络和哈希编码的图像检索方法[J]. 智能系统学报. 2016, 11(3):391-400.
[19] ZHANG R, LIN L, ZHANG R, et al. Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification[J]. IEEE Transactions on Image Processing. 2015, 24(12):4766-4779.
[20] LIONG V E, LU J, WANG G, et al. Deep hashing for compact binary codes learning[C]//Computer Vision And Pattern Recognition. New York:IEEE, 2015:2475-2483.
[21] GUO J, ZHANG S, LI J, et al. Hash learning with convolutional neural networks for semantic based image retrieval[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin:Springer, 2016:227-238.
[22] 向雷, 肖诗斌, 林春雨, 等. 基于轮廓与SIFT特征组合的商标图像检索[J]. 计算机工程与应用. 2013, 49(19):167-172.
[23] ANDONI A, INDYK P, NGUYEN H L, et al. Beyond locality-sensitive hashing[C]//Society for Industrial and Applied Mathematics. Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms. New York:ACM, 2014:1018-1028.
[24] LI H, LIN Z, SHEN X, et al. A convolutional neural network cascade for face detection[C]//Computer Vision And Pattern Recognition. New York:IEEE, 2015:5325-5334.
[25] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报. 2017, 40(6):1229-1251.
[26] KRIZHEVSKY A, SUTSKEVER I, HINTON G E, et al. ImageNet Classification with Deep Convolutional Neural Networks[C]//Conference and Workshop on Neural Information Processing Systems. Cambridge:MIT Press, 2012:1097-1105.
[27] YANG H, LIN K, CHEN C, et al. Supervised learning of semantics-preserving hash via deep convolutional neural networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(2):437-451.
[28] GOOGLE. Tensorflow.[EB/OL]. (2015-04-10)[2018-04-20]. https://tensorflow.google.cn/get-started/.
[29] CHRISTIAN E. Flickrlogos.[EB/OL]. (2011-05-15)[2018-04-10]. https://www.multimedia-computing.de/flickrlogos/data/.
[30] KINGMA D P, BA J. Adam:A method for stochastic optimization[J/OL] CoRR, 2014, abs/1412. 6980:1-15.[2018-05-20]. https://arxiv.org/pdf/1412.6980v2.pdf.

基于深度哈希学习的商标图像检索研究

Research on trademark image retrieval based on deep Hashing

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	刘波, 白晓东, 张更新, 沈俊, 谢继东, 赵来定, 洪涛. 深度学习在认知无线电中的应用研究综述[J]. 华东师范大学学报（自然科学版）, 2021, 2021(1): 36-52.
[2]	张旭, 黄定江. 基于深度学习的铝材表面缺陷检测[J]. 华东师范大学学报（自然科学版）, 2020, 2020(6): 105-114.
[3]	韩程程, 李磊, 刘婷婷, 高明. 语义文本相似度计算方法[J]. 华东师范大学学报（自然科学版）, 2020, 2020(5): 95-112.
[4]	贺小娟, 郭新顺. 基于特征优化的广告点击率预测模型研究[J]. 华东师范大学学报（自然科学版）, 2020, 2020(4): 147-155.
[5]	刘恒宇, 张天成, 武培文, 于戈. 知识追踪综述[J]. 华东师范大学学报(自然科学版), 2019, 2019(5): 1-15.
[6]	陈远哲, 匡俊, 刘婷婷, 高明, 周傲英. 共指消解技术综述[J]. 华东师范大学学报(自然科学版), 2019, 2019(5): 16-35.
[7]	杨康, 黄定江, 高明. 面向自动问答的机器阅读理解综述[J]. 华东师范大学学报(自然科学版), 2019, 2019(5): 36-52.
[8]	叶健, 赵慧. 基于大规模弹幕数据监听和情感分类的舆情分析模型[J]. 华东师范大学学报(自然科学版), 2019, 2019(3): 86-100.
[9]	余若男, 黄定江, 董启文. 基于深度学习的场景文字检测研究进展[J]. 华东师范大学学报(自然科学版), 2018, 2018(5): 1-16.
[10]	金丽娇, 傅云斌, 董启文. 基于卷积神经网络的自动问答[J]. 华东师范大学学报(自然科学版), 2017, 2017(5): 66-79.