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Experiences with Shape Classification through Fuzzy c-Means Using Geometrical and Moments Descriptors

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Book cover Adaptive Multimedia Retrieval. Context, Exploration, and Fusion (AMR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6817))

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Abstract

Due to the growing diffusion of digital media, most of real world applications have data with multiple modalities, from multiple sources and in multiple formats. The modelling of information coming from multimedia sources represents an important issue for applications which achieve multimedia mining activities. In particular, the last decades have witnessed great interest in image processing by “mining” visual information for objects recognition and retrieval. Some studies have revealed the image disambiguation based on the shape produces better results than features such as color or texture; moreover, the classification of objects extracted from an image database appears more intuitively formulated as a shape classification task.

This paper presents an approach for 2D shapes classification. The approach is based on the combined use of geometrical and moments features extracted by a given collection of images and achieves shape-based classification exploiting fuzzy clustering techniques.

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References

  1. Shape data for the mpeg-7 core experiment ce-shape-1, http://www.cis.temple.edu/~latecki/TestData/mpeg7shapeB.tar.gz

  2. Aguilera, A., Suber, A., Martinez, L., Subero, A., Tineo, L.: Fuzzy image retrieval system. In: The 10th IEEE International Conference on Fuzzy Systems, vol. 3, pp. 1247–1250 (2001)

    Google Scholar 

  3. Bezdek, J.: Cluster validity with fuzzy sets. J. Cybernet. 3, 58–72 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bezdek, J.: Numerical taxonomy with fuzzy sets. J. Math. Biol. 1-1, 57–71 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)

    Book  MATH  Google Scholar 

  6. Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: CVPR 2008: Proceedings of the Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  7. Celebi, M.E., Aslandogan, Y.A.: A comparative study of three moment-based shape descriptors. In: ITCC 2005: Proceedings of the International Conference on Information Technology: Coding and Computing, pp. 788–793. IEEE Computer Society, Washington, DC, USA (2005)

    Google Scholar 

  8. Chen, Y., Wang, J.: A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9), 1252–1267 (2002)

    Article  Google Scholar 

  9. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)

    Article  Google Scholar 

  10. Flusser, J., Suk, T.: Pattern recognition by affine moment invariants. Pattern Recognition 26(1), 167–174 (1993)

    Article  MathSciNet  Google Scholar 

  11. Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley, Chichester (2009)

    Book  MATH  Google Scholar 

  12. Frigui, H.: Membershipmap: Data transformation based on granulation and fuzzy membership aggregation. IEEE Transactions on Fuzzy Systems 14(6), 885–896 (2006)

    Article  Google Scholar 

  13. Gao, Y., Fan, J.: Semantic image classification with hierarchical feature subset selection. In: MIR 2005: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 135–142. ACM, New York (2005)

    Google Scholar 

  14. Gevers, T., Smeulders, A.W.M.: Image search engines - an overview. In: Medioni, G., Kang, S.B. (eds.) Emerging Topics in Computer Vision. Prentice-Hall, Englewood Cliffs (2004)

    Google Scholar 

  15. Ha, J.-Y., Kim, G.-Y., Choi, H.-I.: The content-based image retrieval method using multiple features. In: NCM 2008: Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management, pp. 652–657. IEEE Computer Society, Washington, DC, USA (2008)

    Chapter  Google Scholar 

  16. Hall, L., Bensaid, A., Clarke, L., Velthuizen, R., Silbiger, M., Bezdek, J.: A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Networks 3(5), 672–682 (1992)

    Article  Google Scholar 

  17. Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1962)

    Article  MATH  Google Scholar 

  18. Images, Z.C., Zhang, M., Hall, L.O., Muller-karger, F.E., Goldgof, D.B.: Knowledge-guided classification of coastal. International Journal of Pattern Recognition and Artificial Intelligence 14, 987–1007 (2000)

    Google Scholar 

  19. Jingwei, S.E., Eschrich, S., Ke, J., Hall, L.O., Goldgof, D.B.: Fast fuzzy clustering of infrared images. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference (2001)

    Google Scholar 

  20. Krishnapuram, R., Medasani, S., Jung, S.-H., Choi, Y.-S., Balasubramaniam, R.: Content-based image retrieval based on a fuzzy approach. IEEE Transactions on Knowledge and Data Engineering 16(10), 1185–1199 (2004)

    Article  Google Scholar 

  21. Kulkami, S., Verma, B., Sharma, P., Selvaraj, H.: Content based image retrieval using a neuro-fuzzy technique, vol. 6, pp. 4304–4308 (July 1999)

    Google Scholar 

  22. Lay, J.A., Guan, L.: Image retrieval based on energy histograms of the low frequency dct coefficients. In: ICASSP 1999: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 3009–3012. IEEE Computer Society, Washington, DC, USA (1999)

    Google Scholar 

  23. Lieberman, H., Rozenweig, E., Singh, P.: Aria: An agent for annotating and retrieving images. Computer 34, 57–62 (2001)

    Article  Google Scholar 

  24. Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (August 1999)

    Google Scholar 

  25. Nachtegael, M., der Weken, D.V., Witte, V.D., Schulte, S., Mélange, T., Kerre, E.E.: Color image retrieval using fuzzy similarity measures and fuzzy partitions. In: ICIP (6), pp. 13–16 (2007)

    Google Scholar 

  26. Pengyu, L., Kebin, J., Peizhen, Z.: An effective method of image retrieval based on modified fuzzy c-means clustering scheme, vol. 3 (November 2006)

    Google Scholar 

  27. Rath, T.M., Manmatha, R., Lavrenko, V.: A search engine for historical manuscript images. In: SIGIR 2004: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 369–376. ACM, New York (2004)

    Google Scholar 

  28. Rui, Y., Huang, T.S., Chang, S.-F.: Image retrieval: Current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation 10(1), 39–62 (1999)

    Article  Google Scholar 

  29. Tian, Y., Mei, L.: Image retrieval based on multiple features using wavelet. In: ICCIMA 2003: Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications, p. 137. IEEE Computer Society, Washington, DC, USA (2003)

    Google Scholar 

  30. Vertan, C., Boujemaa, N.: Embedding fuzzy logic in content based image retrieval, pp. 85 –89 (2000)

    Google Scholar 

  31. Xie, X., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 841–847 (1991)

    Article  Google Scholar 

  32. Yao, W., Hall, L., Goldgof, D., Muller-Karger, F.: Finding green river in seawifs satellite images. In: International Conference on Pattern Recognition, vol. 2, p. 2307 (2000)

    Google Scholar 

  33. Zhang, D.: Improving image retrieval performance by using both color and texture features. In: ICIG 2004: Proceedings of the Third International Conference on Image and Graphics, pp. 172–175. IEEE Computer Society, Washington, DC, USA (2004)

    Google Scholar 

  34. Zhang, D., Lu, G.: Evaluation of mpeg-7 shape descriptors against other shape descriptors. Multimedia Syst. 9(1), 15–30 (2003)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università della Basilicata, Viale Dell’Ateneo 10, Macchia Romana, 85100, Potenza, Italy

    Ugo Erra

  2. Dipartimento di Matematica e Informatica, Università di Salerno, Via Ponte Don Melillo, 84084, Fisciano, SA, Italy

    Sabrina Senatore

Authors
  1. Ugo Erra
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  2. Sabrina Senatore
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Editor information

Editors and Affiliations

  1. Université Pierre et Marie Curie, CNRS research associate, LIP6, 4 place Jussieu, 75005, Paris, France

    Marcin Detyniecki

  2. Department of Computational Perception, Johannes Kepler University, Altenberger Straße 69, 4040, Linz, Austria

    Peter Knees  & Markus Schedl  & 

  3. Faculty of Computer Science, Otto-von-Guericke University, Universitätsplatz 2, 39406, Magdeburg, Germany

    Andreas Nürnberger

  4. Faculty of Computer Science, Otto-von-Guericke University, Universitätsplatz 2, 39106, Magdeburg, Germany

    Sebastian Stober

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Erra, U., Senatore, S. (2011). Experiences with Shape Classification through Fuzzy c-Means Using Geometrical and Moments Descriptors. In: Detyniecki, M., Knees, P., Nürnberger, A., Schedl, M., Stober, S. (eds) Adaptive Multimedia Retrieval. Context, Exploration, and Fusion. AMR 2010. Lecture Notes in Computer Science, vol 6817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27169-4_14

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  • DOI: https://doi.org/10.1007/978-3-642-27169-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27168-7

  • Online ISBN: 978-3-642-27169-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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