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International Conference on Intelligent Computing

ICIC 2020: Intelligent Computing Theories and Application pp 576–584Cite as

Research on Unstructured Electronic Archives Query Based on Visual Retrieval Technology

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12463))

Abstract

Digitalization of enterprise archives is the mainstream trend of archive management. This paper proposes a digital archive index management framework based on visual retrieval technology for unstructured digital archive management problems. The framework adopts the current mainstream deep local feature extraction scheme DELF Pipeline to carry out feature extraction for digital archives, and use the distributed inverted indexing framework Lucene to build an efficient indexing and retrieval system for digital archives. Through a large number of simulation experiments, it is proved that the framework can be well used for the management of enterprise unstructured digital archives, which supports dynamic incremental index construction and has high retrieval efficiency.

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References

  1. Aihara, K., Takasu, A., Adachi, J.: A distributed index system for efficient query processing in peer-to-peer Networks. Commun. Comput. Signal Process. 1, 139–142 (2003)

    Google Scholar 

  2. Kanwar, R., Trivedi, P., Singh, K.: No SQL, a solution for distributed database management system. Int. J. Comput. Appl. 67(2), 6–9 (2013)

    Google Scholar 

  3. Mansuri, I.R., Sarawagi, S.: Integrating unstructured data into relational databases. In: Proceedings of the 22nd International Conference on Data Engineering (2006)

    Google Scholar 

  4. Dede, E., Sendir, B., Kuzlu, P., et al.: Processing cassandra datasets with Hadoop- streaming based approaches. IEEE Trans. Serv. Comput. 9(1), 46–58 (2016)

    Article  Google Scholar 

  5. Corcoglioniti, F., Rospocher, M., Cattoni, R., et al.: The knowledge store: a storage framework for interlinking unstructured and structured knowledge. Int. J. Semant. Web Info. Syst. 11(2), 1–35 (2015)

    Article  Google Scholar 

  6. Do, B.H., Wu, A., Biswal, S., et al.: Informatics in radiology: RADTF: a semantic search-enabled, natural language processor-generated radiology teaching file. Radio Graph. Rev. Publ. Radiol. Soc. North America Inc. 30(7), 2039–2048 (2010)

    Google Scholar 

  7. Wu, Q., Ma, S., Liu, Y.: Sub-event discovery and retrieval during natural hazards on social media data. World Wide Web 19(2), 277–297 (2015). https://doi.org/10.1007/s11280-015-0359-8

    Article  Google Scholar 

  8. Yang, J., Jiang, B., Li, B., et al.: A fast image retrieval method designed for network big data. IEEE Trans. Ind. Inform. PP(99), 1 (2017)

    Google Scholar 

  9. Weng, C.-C., Chen, H., Fuh, C.-S.: A novel automatic white balance method for digital still cameras. In: IEEE International Symposium on Circuits & Systems, 26 June 2005

    Google Scholar 

  10. Wang, Q., et al.: Image classification based on deep local feature coding. In: International Symposium on Intelligent Signal Processing & Communication Systems (2017)

    Google Scholar 

  11. Lan, Z., Zhu, Y., Hauptmann, A.G.: Deep local video feature for action recognition (2017)

    Google Scholar 

  12. Zhu, Q., et al.: A deep-local-global feature fusion framework for high spatial resolution imagery scene classification. Remote Sens. 10(4), 568 (2018)

    Article  Google Scholar 

  13. David, G.L.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Corfu, Greece, September 1999, pp. 1150–1157 (1999)

    Google Scholar 

  14. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  15. Rublee, E., et al.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision IEEE (2012)

    Google Scholar 

  16. Noh, H., et al.: Large-scale image retrieval with attentive deep local features. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)

    Google Scholar 

  17. Zhou, D., Xie, K.: Lucene search engine. Comput. Eng. 33(18), 95–97 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. E-Energy Tech Ltd., Hangzhou, Zhejiang, China

    Hui Yang

Authors
  1. Hui Yang
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Corresponding author

Correspondence to Hui Yang .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Yang, H. (2020). Research on Unstructured Electronic Archives Query Based on Visual Retrieval Technology. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_52

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  • DOI: https://doi.org/10.1007/978-3-030-60799-9_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60798-2

  • Online ISBN: 978-3-030-60799-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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