Skip to main content
SpringerLink
Log in

A VCA-Based Approach to Enhance Learning Data Sets for Object Classification

  • Conference paper
  • First Online:
Multimedia Communications, Services and Security (MCSS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1284))

  • 432 Accesses

Abstract

This paper presents a novel approach to solving the problem of poor learning data in complex object classification task. It efficiently combines the Visual Content Analysis technique known as the Scalable Vocabulary Tree (SVT) and contour-based descriptors to recommend new training samples. The SVT technique uses the SIFT features to identify and accurately localize objects of interest within the visual content of the processed query images. Despite the small learning data set its classification accuracy is pretty good and matches the accuracy of a dedicated CNN network trained under the same conditions. However, due to the ability of fast and effective incremental learning, it overcomes the convnet type networks. Contour-based classification based on Point Distance Histogram (PDH) is utilized then to increase the classification certainty. During this stage, the PDH descriptors representing a given object of interest are matched against descriptors stored in the pattern database, where each object is represented by a collection of 360 pattern outlines extracted from its 3D model. As finally reported, such an exact pattern representation allows for achieving a high classification accuracy of the entire approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://ai.facebook.com/.

  2. 2.

    https://lens.google.com/.

  3. 3.

    https://aws.amazon.com/rekognition/.

  4. 4.

    https://cloudsight.ai/.

  5. 5.

    https://hbr.org/2018/04/if-your-data-is-bad-your-machine-learning-tools-are-useless.

  6. 6.

    https://hbr.org/2016/12/breaking-down-data-silos?autocomplete=true.

  7. 7.

    http://www.doosanbabcock.com/pl/intro/projekt-inred/.

  8. 8.

    https://keras.io/.

  9. 9.

    https://www.tensorflow.org/neural_structured_learning/framework.

  10. 10.

    http://www.image-net.org/.

  11. 11.

    https://docs.opencv.org/master/db/d39/classcv_1_1DescriptorMatcher.html.

  12. 12.

    https://www.autodesk.co.uk/.

  13. 13.

    .https://www.power-technology.com/features/featurebuilding-a-giant-3d-power-plant-design/.

  14. 14.

    https://3dprintingcenter.net/.

  15. 15.

    http://www.doosanheavy.com/en/intro/digital-transformation/plannbuild/.

References

  1. http://www.live-counter.com/how-big-is-the-internet/. Accessed 26 Feb 2020

  2. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, pp. 1701–1708 (2014). https://doi.org/10.1109/cvpr.2014.220

  3. Chollet, F.: Deep Learning with Python. Manning Publications Co., New York (2018)

    Google Scholar 

  4. Worring, M., Snoek, C.: Visual content analysis. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp 3360–3365. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9_1019

    Chapter  Google Scholar 

  5. Baran, R., Zeja, A.: The IMCOP system for data enrichment and content discovery and delivery. In: Proceedings of the 2015 International Conference on Computational Science and Computational Intelligence (CSCI 2015), pp 143–146, Las Vegas, USA (2015)

    Google Scholar 

  6. Wolff, E.: Microservices: Flexible Software Architectures. Addison-Wesley, Boston (2016)

    Google Scholar 

  7. Li, Z., Hoiem, D.: Learning without forgetting. PAMI 40, 2935–2947 (2018)

    Article  Google Scholar 

  8. Tao, Y., Tu Y., Shyu, M..: Efficient incremental training for deep convolutional neural networks. In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA, pp. 286–291 (2019)

    Google Scholar 

  9. Cheng, Y., Wang, D., Zhou, P., Zhang, T.: A survey of model compression and acceleration for deep neural networks, CoRR, vol. abs/1710.09282 (2017)

    Google Scholar 

  10. Roy, D., Panda, P., Roy, K.: Tree-CNN: a hierarchical deep convolutional neural network for incremental learning. Neural Netw. 121, 148–160 (2018)

    Article  Google Scholar 

  11. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Conference on Computer Vision and Pattern Recognition, New York, NY, USA, pp. 2161–2168 (2006)

    Google Scholar 

  12. Baran, R.: Efficiency investigation of BoF, SVT and pyramid match algorithms in practical recognition applications. In: Proceedings of the 2017 IEEE International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), pp 171–178, Corfu Island, Greece (2017)

    Google Scholar 

  13. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the ICCV 1999, vol 2, pp 1150–1157. IEEE Computer Society (1999)

    Google Scholar 

  14. Rublee, E., Rabaud, V., Konolige, K., Bradski, G. R.: ORB: an efficient alternative to SIFT or SURF. In: ICCV 2011, pp. 2564–2571 (2011)

    Google Scholar 

  15. Baran, R., Rudziński, F., Zeja, A.: Face recognition for movie character and actor discrimination based on similarity scores. In: Proceedings of the 2016 IEEE International Conference on Computational Science and Computational Intelligence (CSCI), pp 1333–1338, Las Vegas, USA (2016)

    Google Scholar 

  16. Rother, C., Kolmogorov, V., Blake, A.: GrabCut - interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)

    Article  Google Scholar 

  17. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  18. Baran, R., Kleszcz, A.: The efficient spatial methods of contour approximation. In: Proceedings of the 2014 IEEE International Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA 2014), pp. 116–121, Poznań, Poland (2014)

    Google Scholar 

  19. Frejlichowski, D.: Shape representation using point distance histogram. Polish J. Environ. Stud. 16(4A), 90–93 (2007)

    Google Scholar 

  20. Frejlichowski, D.: application of the point distance histogram to the automatic identification of people by means of digital dental radiographic images. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 387–394. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46418-3_34

    Chapter  Google Scholar 

Download references

Acknowledgments

This work was supported by the Polish National Centre for Research and Development under the Smart Growth Operational Programme, INRED project no. POIR.01.01.01-00-0170/17. We want also to address our special thanks to our colleagues Iwo Ryszkowski and Krzysztof Nowakowski from AGH University of Science and Technology (Poland) for their valuable contributions to this work.

Author information

Authors and Affiliations

  1. Department of Computer Science, Electronics and Electrical Engineering, Kielce University of Technology, Kielce, Poland

    Remigiusz Baran

  2. Department of Teleinformatics, University of Computer Engineering and Telecommunications, Kielce, Poland

    Andrzej Zeja

Authors
  1. Remigiusz Baran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrzej Zeja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Remigiusz Baran .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Kraków, Poland

    Andrzej Dziech

  2. Royal Military Academy, Brussels, Belgium

    Wim Mees

  3. Gdańsk University of Technology, Gdańsk, Poland

    Andrzej Czyżewski

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Baran, R., Zeja, A. (2020). A VCA-Based Approach to Enhance Learning Data Sets for Object Classification. In: Dziech, A., Mees, W., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2020. Communications in Computer and Information Science, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-59000-0_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59000-0_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58999-8

  • Online ISBN: 978-3-030-59000-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation