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Spatial Pyramid Block for Oracle Bone Inscription Detection

Published: 17 April 2020 Publication History

Abstract

The detection of Oracle Bone Inscription (OBI) is one of the most fundamental aspects of oracle bone morphology. However, the detection method depending on experts' experience requires longterm learning and accumulation for professional knowledge. This paper investigated the performance of the deep-learning-based object detection framework in the OBI dataset, then selected the one with the best performance as the baseline and made a series of optimization. Specifically, we first redesigned the sizes and ratios of the anchor box according to the data characteristics by using K- means clustering. Secondly, we extracted some typical noises from OBI for data augmentation. Finally, Focal Loss and Mixed-precision are used to improve the model precision and compress the memory footprint. To further improve the performance, the Spatial Pyramid Block is proposed, which can stabilize features and suppress noise interference. Experiments on our OBI benchmarks validate the superiority of the proposed method that achieves 82.1% F-measure suppressing several mainstream object detectors. Our dataset and algorithms will soon be available at http://jgw.aynu.edu.cn.

References

[1]
Li, F., & Zhou, X. (1996). Recognition of Jia Gu Wen based on Graph theory. Journal of Electronics, 18(suppl.), 41-[2] Zhou, X., Li, F., Hua, X., & Wei, J. (1996). A method of Jia
[2]
Gu Wen recognition based on a two-level classification. Journal of Fudan University (Natural Science), 35(5), 481--486.
[3]
Meng, L., & Izumi, T. (2017). A combined recognition system for oracle bone inscriptions. Int. J. Mechatron. Syst., 7(4), 235--244.
[4]
Li, Q., Yang, Y., & Wang, A. (2011). Recognition of inscriptions on bones or tortoise shells based on graph isomorphism. Computer Engineering and Applications, 47(8), 112--114.
[5]
Lv, X., Li, M., Cai, K., Wang, X., & Tang, Y. (2010). A graphic-based method for Chinese oracle-bone classification. Journal of Beijing Information Science and Technology University, 25(Z2), 92--96.
[6]
Meng, L. (2017). Two-stage recognition for oracle bone inscriptions. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10485, 672--682.
[7]
Meng, L., & Izumi, T. (2017). A combined recognition system for oracle bone inscriptions. Int. J. Mechatron. Syst., 7(4), 235--244.
[8]
Meng, L. (2017). Recognition of oracle bone inscriptions by extracting line features on image processing. Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (pp. 606--611).
[9]
Feng, G., Gu, S., & Yang, Y. (2013). Feature extraction method of Oracle-bone inscriptions based on mathematical morphology. Journal of Chinese Information Processing, 27(2), 79--85.
[10]
Guo, C., Wang, E., Roman, R., Chao, H., & Rui, Y. (2016). Building hierarchical representations for oracle character and sketch recognition. IEEE Trans. Image Process., 25(1), 104--118.
[11]
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. (2015)
[12]
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European conference on computer vision, Springer (2016) 21--37
[13]
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
[14]
Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2018) 4203--4212
[15]
Liu, S., Huang, D., et al.: Receptive field block net for accurate and fast object de-tection. In: Proceedings of the European Conference on Computer Vision (ECCV). (2018) 385--400
[16]
Arthur, D., and Vassilvitskii, S. 2007. k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM sympo- sium on Discrete algorithms, 1027--1035. Society for Indus- trial and Applied Mathematics.58. Guo, C., Wang, E., Roman, R., Chao, H., & Rui, Y. (2016). Building hierarchical representations for oracle character and sketch recognition. IEEE Trans. Image Process., 25(1), 104--118.
[17]
Lin T Y, Goyal P, Girshick R, et al. Focal Loss for Dense Object Detection[J]. IEEE Transactions on Pattern Analysis
[18]
Bordes, J.; Maher, M.; and Sechrest, M. 2009. Nvidia apex: High definition physics with clothing and vegetation. In Game Developers Confer- ence.
[19]
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 37 (2015) 1904--1916
[20]
Long, S., He, X., Ya, C.: Scene text detection and recognition: The deep learning era. arXiv preprint arXiv:1811.04256 (2018)
[21]
Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE (2010) 2963--2970
[22]
Huang, W., Lin, Z., Yang, J., Wang, J.: Text localization in natural images using stroke feature transform and text covariance descriptors. In: Proceedings of the IEEE International Conference on Computer Vision. (2013) 1241--1248
[23]
Jain, A.K., Yu, B.: Automatic text location in images and video frames. Pattern recognition 31 (1998) 2055--2076
[24]
Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: 2012 IEEEConference on Computer Vision and Pattern Recognition, IEEE (2012) 1083--1090
[25]
Yi, C., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. IEEE Transactions on Image Processing 20 (2011) 2594--2605
[26]
Yin, X.C., Yin, X., Huang, K., Hao, H.W.: Robust text detection in natural scene images. IEEE transactions on pattern analysis and machine intelligence 36 (2014) 970--983
[27]
Coates, A., Carpenter, B., Case, C., Satheesh, S., Suresh, B., Wang, T., Wu, D.J., Ng, A.Y.: Text detection and character recognition in scene images with unsuper-vised feature learning. In: ICDAR. Volume 11. (2011) 440--445
[28]
Lee, J.J., Lee, P.H., Lee, S.W., Yuille, A., Koch, C.: Adaboost for text detection in natural scene. In: 2011 International Conference on Document Analysis and Recognition, IEEE (2011) 429--434
[29]
Wang, K., Babenko, B., Belongie, S.: End-to-end scene text recognition. In: 2011 International Conference on Computer Vision, IEEE (2011) 1457--1464
[30]
Wang, T., Wu, D.J., Coates, A., Ng, A.Y.: End-to-end text recognition with convolutional neural networks. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), IEEE (2012) 3304--3308
[31]
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2017) 7310--7311
[32]
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. International journal of computer vision 104 (2013) 154--171
[33]
Zitnick, C.L., Doll' ar, P.: Edge boxes: Locating object proposals from edges. In: European conference on computer vision, Springer (2014) 391--405
[34]
Pinheiro, P.O., Collobert, R., Doll' ar, P.: Learning to segment object candidates. In: Advances in Neural Information Processing Systems. (2015) 1990--1998
[35]
Pinheiro, P.O., Lin, T.Y., Collobert, R., Doll' ar, P.: Learning to refine object segments. In: European Conference on Computer Vision, Springer (2016) 75--91
[36]
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for ac-curate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2014) 580--587
[37]
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 37 (2015) 1904--1916
[38]
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision. (2015) 1440--1448
[39]
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. International journal of computer vision 111 (2015) 98--136
[40]
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll' ar, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision, Springer (2014) 740--755
[41]
Dai, J., Li, Y., He, K., Sun, J.: R-fcn: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems. (2016) 379--387
[42]
Lee, H., Eum, S., Kwon, H.: Me r-cnn: Multi-expert r- cnn for object detection. arXiv preprint arXiv:1704.01069 (2017)
[43]
Zhu, Y., Zhao, C., Wang, J., Zhao, X., Wu, Y., Lu, H.: Couplenet: Coupling global structure with local parts for object detection. In: Proceedings of the IEEE International Conference on Computer Vision. (2017) 4126--4134
[44]
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: IEEE Conference on Computer Vision Pat-tern Recognition. (2016)
[45]
Wang, X., Shrivastava, A., Gupta, A.: A-fast-rcnn: Hard positive generation via adversary for object detection. (2017)
[46]
Bell, S., Lawrence Zitnick, C., Bala, K., Girshick, R.: Insideoutside net: Detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2016) 2874--2883
[47]
Gidaris, S., Komodakis, N.: Object detection via a multi-region and semantic segmentation-aware cnn model. In: Proceedings of the IEEE International Confer-ence on Computer Vision. (2015) 1134--1142
[48]
Shrivastava, A., Gupta, A.: Contextual priming and feedback for faster r-cnn. In: European Conference on Computer Vision, Springer (2016) 330--348
[49]
Zeng, X., Ouyang, W., Yang, B., Yan, J., Wang, X.: Gated bidirectional cnn for object detection. In: European Conference on Computer Vision, Springer (2016) 354--369
[50]
Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep con-volutional neural network for fast object detection. In: European conference on computer vision, Springer (2016) 354--370
[51]
Kong, T., Sun, F., Yao, A., Liu, H., Lu, M., Chen, Y.: Ron: Reverse connection with objectness prior networks for object detection. (2017)
[52]
Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision Pattern Recognition. (2017)
[53]
Shrivastava, A., Sukthankar, R., Malik, J., Gupta, A.: Beyond skip connections: Top-down modulation for object detection. (2016)
[54]
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., Lecun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. Eprint Arxiv (2013)
[55]
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. (2015)
[56]
Redmon, J., Farhadi, A.: Yolo9000: Better, faster, stronger. In:IEEE Conference on Computer Vision Pattern Recognition. (2017)
[57]
Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C.: Dssd: Deconvolutional single shot detector. (2017)
[58]
Shen, Z., Zhuang, L., Li, J., Jiang, Y.G., Chen, Y., Xue, X.: Dsod: Learning deeply supervised object detectors from scratch. (2017)
[59]
Paszke A, Gross S, Chintala S, et al. Automatic differentiation in pytorch[J]. 2017.
[60]
Yao, W.; Sun, Z.; and Chen, X. 2019. Understanding video content: Efficient hero detec- tion and recognition for the game" honor of kings". arXiv preprint arXiv:1907.07854.
[61]
Laroca, R.; Zanlorensi, L. A.; Gonc alves, G. R.; Todt, E.; Schwartz, W. R.; and Menotti, D. 2019. An efficient and layout-independent automatic license plate recognition system based on the yolo detector. arXiv preprint arXiv:1909.01754.
[62]
Simon M, Amende K, Kraus A, et al. Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019: 0-0.

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  1. Spatial Pyramid Block for Oracle Bone Inscription Detection

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    cover image ACM Other conferences
    ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications
    February 2020
    382 pages
    ISBN:9781450376655
    DOI:10.1145/3384544
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    Publication History

    Published: 17 April 2020

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    Author Tags

    1. Bone
    2. Detection
    3. Inscription
    4. Oracle

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    • National Natural Science Foundation of China

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    • (2024)OBCTeacherInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10386461:6Online publication date: 1-Nov-2024
    • (2024)An unsupervised automatic organization method for Professor Shirakawa’s hand-notated documents of oracle bone inscriptionsInternational Journal on Document Analysis and Recognition10.1007/s10032-024-00463-027:4(583-601)Online publication date: 1-Dec-2024
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