[1]XU Boxiang,LIU Li,QIU Taorong.Near-duplicate document image retrieval based on three-stream convolutional Siamese network[J].CAAI Transactions on Intelligent Systems,2022,17(3):515-522.[doi:10.11992/tis.202105018]
Copy

Near-duplicate document image retrieval based on three-stream convolutional Siamese network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 3 Page number: 515-522 Column: 学术论文—机器感知与模式识别 Public date: 2022-05-05
Title:
Near-duplicate document image retrieval based on three-stream convolutional Siamese network
Author(s):
XU Boxiang;  LIU Li;  QIU Taorong
School of Information Engineering, Nanchang University, Nanchang 330031, China
Keywords:
near-duplicate document image; image retrieval; three-stream convolutional Siamese network; triplet loss; image variations; triplet; feature extraction; robustness
CLC:
TP391
DOI:
10.11992/tis.202105018
Abstract:
In the traditional near-duplicate document image retrieval methods, the variations among the near-duplicate document images had to be manually identified beforehand, which can be easily influenced by human subjectivity. To solve this problem, we propose a three-stream convolutional Siamese network orienting toward the near-duplicate text-image retrieval, which can automatically learn the variation types among the near-duplicate document images. The input to this network is a triplet, consisting of a query image, its near-duplicate image, and its non-near-duplicate image. Using the triplet loss, the distance between the query image and its near-duplicate image is guaranteed to be smaller than that between the query and its non-near-duplicate image. This approach achieves promising results with the mAP of 98.76% and 96.50% on two datasets, respectively, thereby greatly outperforming the state-of-the-art near-duplicate document image retrieval methods.
References:
[1] 方涛, 陈志国, 傅毅. 神经网络多层特征信息融合的人脸识别方法[J]. 智能系统学报, 2021, 16(2): 279–285
FANG Tao, CHEN Zhiguo, FU Yi. Face recognition method based on neural network multi-layer feature information fusion[J]. CAAI transactions on intelligent systems, 2021, 16(2): 279–285
[2] WANG Zi, LI Chengcheng, WANG Xiangyang. Convolutional neural network pruning with structural redundancy reduction[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021: 14913–14922.
[3] SPITZ L. Duplicate document detection[C]//Proceedings of Document Recognition IV. San Jose, USA, 1997: 88–94.
[4] HULL J J. Document image matching and retrieval with multiple distortion-invariant descriptors[J].Proceedings of the international workshop on document analysis systems, 1994: 379–396.
[5] NAKAI T, KISE K, IWAMURA M. Real-time retrieval for images of documents in various languages using a web camera[C]//2009 10th International Conference on Document Analysis and Recognition. Barcelona: IEEE, 2009: 146–150.
[6] MORALEDA J. Large scalability in document image matching using text retrieval[J]. Pattern recognition letters, 2012, 33(7): 863–871.
[7] LIU Li, LU Yue, SUEN C Y, et al. Modeling local word spatial configurations for near duplicate document image retrieval[C]//2013 12th International Conference on Document Analysis and Recognition. Washington: IEEE, 2013: 235–239.
[8] LIU Li, LU Yue, SUEN C Y. Near-duplicate document image matching: a graphical perspective[J]. Pattern recognition, 2014, 47(4): 1653–1663.
[9] LIU Li, LU Yue, SUEN C Y. Document image matching using probabilistic graphical models[C]//Proceedings of the 21st International Conference on Pattern Recognition. Tsukuba, Japan, 2012: 637–640.
[10] BELLAVIA F, COLOMBO C. Is there anything new to say about SIFT matching?[J]. International journal of computer vision, 2020, 128(7): 1847–1866.
[11] VITALADEVUNI S, CHOI F, PRASAD R, et al. Detecting near-duplicate document images using interest point matching[C]//Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Tsukuba: IEEE, 2012: 347–350.
[12] ROYER E, BOUCHARA F. Guiding text image keypoints extraction through layout analysis[C]//2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Kyoto: IEEE, 2017: 9–14.
[13] SUN Yanan, XUE Bing, ZHANG Mengjie, et al. Automatically designing CNN architectures using the genetic algorithm for image classification[J]. IEEE transactions on cybernetics, 2020, 50(9): 3840–3854.
[14] JI Yuzhu, ZHANG Haijun, ZHANG Zhao, et al. CNN-based encoder-decoder networks for salient object detection: a comprehensive review and recent advances[J]. Information sciences, 2021, 546: 835–857.
[15] BABENKO A, SLESAREV A, CHIGORIN A, et al. Neural codes for image retrieval[C]//European Conference on Computer Vision. Cham:Springer, 2014: 584–599.
[16] BABENKO A, LEMPITSKY V. Aggregating deep convolutional features for image retrieval[C]//Proceedings of International Conference on Computer Vision. Santiago: IEEE, 2015: 1269–1277.
[17] MIN Weiqing, MEI Shuhuan, LI Zhou, et al. A two-stage triplet network training framework for image retrieval[J]. IEEE transactions on multimedia, 2020, 22(12): 3128–3138.
[18] HUSAIN S S, ONG E J, BOBER M. ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval[J]. International journal of computer vision, 2021, 129(5): 1432–1450.
[19] GORDO A, ALMAZáN J, REVAUD J, et al. End-to-end learning of deep visual representations for image retrieval[J]. International journal of computer vision, 2017, 124(2): 237–254.
[20] TOLIAS G, SICRE R, JéGOU H. Particular object retrieval with integral max-pooling of CNN activations[EB/OL]//(2015–11–18)[2021–05–13]https://arxiv.org/abs/1511.05879.
[21] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].(2014–09–4)[2021–05–13]https://arxiv.org/abs/1409.1556.
[22] ZHANG Shengdong, NEZHADARYA E, FASHANDI H, et al. Stochastic whitening batch normalization[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021: 10978–10987.
[23] LIU Guanghai, YANG Jingyu. Deep-seated features histogram: a novel image retrieval method[J]. Pattern recognition, 2021, 116: 107926.
[24] TAKEDA K, KISE K, IWAMURA M. Real-time document image retrieval on a smartphone[C]//2012 10th IAPR International Workshop on Document Analysis Systems. Gold Coast: IEEE, 2012: 225–229.
[25] HARLEY A W, UFKES A, DERPANIS K G. Evaluation of deep convolutional nets for document image classification and retrieval[C]//2015 13th International Conference on Document Analysis and Recognition (ICDAR). Tunis: IEEE, 2015: 991–995.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems