Skip to main content
SpringerLink
Log in

Modeling of High-Dimensional Data for Applications of Image Segmentation in Image Retrieval and Recognition Tasks

  • Chapter
  • First Online:
Hybrid Soft Computing for Image Segmentation

  • 824 Accesses

Abstract

Probabilistic Features Combination method (PFC ), is the approach of multidimensional data modeling, extrapolation and interpolation using the set of high-dimensional feature vectors. This method is a hybridization of numerical methods and probabilistic methods with N-dimensional data interpolation for feature vectors. Each feature is treated as a random variable.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Marti, U.-V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recognit. 5, 39–46 (2002)

    Article  MATH  Google Scholar 

  2. Djeddi, C., Souici-Meslati, L.: A texture based approach for Arabic writer identification and verification. In: International Conference on Machine and Web Intelligence, pp. 115–120 (2010)

    Google Scholar 

  3. Djeddi, C., Souici-Meslati, L.: Artificial immune recognition system for Arabic writer identification. In: International Symposium on Innovation in Information and Communication Technology, pp. 159–165 (2011)

    Google Scholar 

  4. Nosary, A., Heutte, L., Paquet, T.: Unsupervised writer adaption applied to handwritten text recognition. Pattern Recogn. Lett. 37(2), 385–388 (2004)

    Article  MATH  Google Scholar 

  5. Bulacu, M., Schomaker, L., Brink, A.: Text-independent writer identification and verification on off-line Arabic handwriting. In: International Conference on Document Analysis and Recognition, pp. 769–773 (2007)

    Google Scholar 

  6. Chen, J., Cheng, W., Lopresti, D.: Using perturbed handwriting to support writer identification in the presence of severe data constraints. In: Document Recognition and Retrieval, pp. 1–10 (2011)

    Google Scholar 

  7. Chen, J., Lopresti, D., Kavallieratou, E.: The impact of ruling lines on writer identification. In: International Conference on Frontiers in Handwriting Recognition, pp. 439–444 (2010)

    Google Scholar 

  8. Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graphics Image Process. 4(2), 172–179 (1975)

    Article  Google Scholar 

  9. Garain, U., Paquet, T.: Off-line multi-script writer identification using AR coefficients. In: International Conference on Document Analysis and Recognition, pp. 991–995 (2009)

    Google Scholar 

  10. Ghiasi, G., Safabakhsh, R.: Offline text-independent writer identification using codebook and efficient code extraction methods. Image Vis. Comput. 31, 379–391 (2013)

    Article  Google Scholar 

  11. Ozaki, M., Adachi, Y., Ishii, N.: Examination of effects of character size on accuracy of writer recognition by new local arc method. In: International Conference on Knowledge-Based Intelligent Information and Engineering Systems, pp. 1170–1175 (2006)

    Google Scholar 

  12. Schlapbach, A., Bunke, H.: A writer identification and verification system using HMM based recognizers. Pattern Anal. Appl. 10, 33–43 (2007)

    Article  MathSciNet  Google Scholar 

  13. Schomaker, L., Franke, K., Bulacu, M.: Using codebooks of fragmented connected- component contours in forensic and historic writer identification. Pattern Recogn. Lett. 28(6), 719–727 (2007)

    Article  Google Scholar 

  14. Shahabinejad, F., Rahmati, M.: A new method for writer identification and verification based on Farsi/Arabic handwritten texts, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 829–833 (2007)

    Google Scholar 

  15. Siddiqi, I., Cloppet, F., Vincent, N.: Contour based features for the classification of ancient manuscripts. In: Conference of the International Graphonomics Society, pp. 226–229 (2009)

    Google Scholar 

  16. Siddiqi, I., Vincent, N.: Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recogn. Lett. 43(11), 3853–3865 (2010)

    Article  MATH  Google Scholar 

  17. Van, E.M., Vuurpijl, L., Franke, K., Schomaker, L.: The WANDA measurement tool for forensic document examination. J. Forensic Doc. Exam. 16, 103–118 (2005)

    Google Scholar 

  18. Schlapbach, A., Bunke, H.: Off-line writer identification using Gaussian mixture models. In: International Conference on Pattern Recognition, pp. 992–995 (2006)

    Google Scholar 

  19. Schlapbach, A., Bunke, H.: Using HMM based recognizers for writer identification and verification. In: 9th International Workshop on Frontiers in Handwriting Recognition, pp. 167–172 (2004)

    Google Scholar 

  20. Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 701–717 (2007)

    Article  Google Scholar 

  21. Chapra, S.C.: Applied Numerical Methods. McGraw-Hill, Boston (2012)

    Google Scholar 

  22. Collins II, G.W.: Fundamental Numerical Methods and Data Analysis. Case Western Reserve University (2003)

    Google Scholar 

  23. Ralston, A., Rabinowitz, P.: A First Course in Numerical Analysis, 2nd edn. Dover Publications, New York (2001)

    MATH  Google Scholar 

  24. Schumaker, L.L.: Spline Functions: Basic Theory. Cambridge Mathematical Library (2007)

    Google Scholar 

  25. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 1(37), 1–19 (2004)

    Article  Google Scholar 

  26. Jakobczak, D.J.: 2D Curve Modeling via the Method of Probabilistic Nodes Combination - Shape Representation, Object Modeling and Curve Interpolation-Extrapolation with the Applications. LAP Lambert Academic Publishing, Saarbrucken (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Science, Koszalin University of Technology, Sniadeckich 2, 75-453, Koszalin, Poland

    Dariusz Jakóbczak

Authors
  1. Dariusz Jakóbczak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dariusz Jakóbczak .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Computer Science and Engineering, Cooch Behar Govt. Engineering College, Cooch Behar, West Bengal, India

    Sourav De

  4. Department of Strategic Development, Raiffeisenbank Austria, Zagreb, Croatia

    Goran Klepac

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this chapter

Cite this chapter

Jakóbczak, D. (2016). Modeling of High-Dimensional Data for Applications of Image Segmentation in Image Retrieval and Recognition Tasks. In: Bhattacharyya, S., Dutta, P., De, S., Klepac, G. (eds) Hybrid Soft Computing for Image Segmentation. Springer, Cham. https://doi.org/10.1007/978-3-319-47223-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47223-2_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47222-5

  • Online ISBN: 978-3-319-47223-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation