Skip to main content
SpringerLink
Log in

Image Segmentation Using Clustering Methods

  • Conference paper
  • First Online:
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 16))

Included in the following conference series:

Abstract

Image segmentation is one of the most valuable processes in the field of artificial vision. It aims to extract objects from the scene by finding patterns between adjacent pixels. In this paper, we introduce a novel approach of object extraction by combining two well-known clustering methods: The K-means and DBSCAN. The aim is to explore the advantages of each one to minimize the mutual inconvenient. The mainidea is the use of two sources in the clusters construction: center homogeneity guided by the K-means algorithm and border homogeneity guided by the DBSCAN algorithm. Results are run on the BSDS500 dataset showing encouraging results comparing to the ground truth segmentation provided.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Smiti, A., Eloudi, Z.: Soft DBSCAN: improving DBSCAN clustering method using fuzzy set theory. In: 2013 6th International Conference on Human System Interactions (HSI) (2013)

    Google Scholar 

  2. Ester, M., Kriegel, H.-P.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge and Discovery, pp. 226–231 (1996)

    Google Scholar 

  3. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. Paper presented at the meeting of the Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  4. Jain, A.K.: Data clustering: 50 years beyond K-means. In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, pp. 3–4 (2008)

    Google Scholar 

  5. Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour Detection and Hierarchical Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

  6. Ray, S., Turi, R.: Determination of number of clusters in K-means clustering and application in colour image segmentation. In: Pal, N., De, A.K., Das, J. (eds.) Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT 1999), pp. 27–29 (1999)

    Google Scholar 

  7. Pelleg, D., Moore, A.: X-means: extending K-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference Machine Learning (ICML 2000), pp. 727–734 (2000)

    Google Scholar 

  8. Feng, Y., Hamerly, G.: PG-Means: learning the number of clusters in data. In: Advances in Neural Information Processing Systems, 19 (NIPS Proceeding), pp. 393–400. MIT Press, Cambridge (2006)

    Google Scholar 

  9. Hamerly, G., Elkan, C.: Learning the K in K-means. Technical report CS2002-0716, University of California, San Diego (2002)

    Google Scholar 

  10. Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. Royal Stat. Soc. Ser. B (Statistical Methodology) 63(2), 411–423 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  11. Birant, D., Kut, A.: ST-DBSCAN: an algorithm for clustering spatial–temporal Data. Data Knowl. Eng. 60(1), 208–221 (2007)

    Article  Google Scholar 

  12. Viswanath, P., Suresh Babu, V.: Rough-DBSCAN: a fast hybrid density based clustering method for large data sets. Pattern Recogn. Lett. 30(16), 1477–1488 (2009)

    Article  Google Scholar 

  13. Kisilevich, S., Mansmann, F., Keim, D.: P-DBSCAN. In: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research and Application, COM.Geo 2010 (2010)

    Google Scholar 

  14. Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Advances in Knowledge Discovery and Data Mining Lecture Notes in Computer Science, pp. 160–172 (2013)

    Google Scholar 

  15. Viswanath, P., Pinkesh, R.: L-DBSCAN: a fast hybrid density based clustering method. In: 18th International Conference on Pattern Recognition (ICPR 2006) (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Science and Technology Houari Boumediene, Algiers, Algeria

    Benrais Lamine & Baha Nadia

Authors
  1. Benrais Lamine
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baha Nadia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benrais Lamine .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Lamine, B., Nadia, B. (2018). Image Segmentation Using Clustering Methods. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56991-8_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56990-1

  • Online ISBN: 978-3-319-56991-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation