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Self-organizing Map (SOM) Based Data Navigation for Identifying Shape Similarities of Graphic Logos

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Abstract

In this paper, we propose a data navigation approach for identifying the shape similarity of graphic logo images using enhanced SOM based visualization methods. Existing SOM based visualization methods have the limitation of not being able to show detailed local distance information and global similarity of data at the same time. Therefore, we propose two visualization approaches to overcome this limitation for better image data navigation. In our experiments, we used MPEG-7 shape image dataset and classic IRIS dataset to demonstrate our approaches are superior to previous approaches in providing sufficient local and global information for data visual navigation.

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References

  1. De Runz C, Desjardin E, Herbin M (2012) Unsupervised visual data mining using self-organizing maps and a data-driven color mapping. In: 16th international conference on information visualisation (IV), pp 241–245 doi: 10.1109/IV.2012.48

  2. Ebrahim Y, Ahmed M, Chau S, Abdelsalam W (2007) An efficient shape representation and description technique. IEEE international conference on image processing, 2007 (ICIP 2007) vol 6, VI-441–VI-444

  3. FISHER RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7(2):179–188. doi:10.1111/j.1469-1809.1936.tb02137.x

    Article  Google Scholar 

  4. Himberg J (2000) A som based cluster visualization and its application for false coloring. In: FInternational joint conference on neural networks (IJCNN), pp 587–592 doi: 10.1109/IJCNN.2000.861379

  5. Kaski S, Venna J, Kohonen T (1999) Coloring that reveals high-dimensional structures in data. In: 6th international conference on neural information processing (ICONIP), vol 2, p 729–734 doi:10.1109/ICONIP.1999.845686

  6. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480

    Article  Google Scholar 

  7. Kraaijveld M (1992) A non-linear projection method based on kohonen’s topology preserving maps. In: 11th IAPR international conference on pattern recognition methodology and systems, proceedings, vol 2, 41–45

  8. MPEG-7: Shape dataset. http://www.cis.temple.edu/latecki/TestData/mpeg7shapeB.tar.gz

  9. Polzlbauer G, Dittenbach M, Rauber A (2005) A visualization technique for self-organizing maps with vector fields to obtain the cluster structure at desired levels of detail. Int Jt Conf Neural Netw (IJCNN) 3:1558–1563. doi:10.1109/IJCNN.2005.1556110

    Google Scholar 

  10. Tasdemir K (2010) Graph based representations of density distribution and distances for self-organizing maps. IEEE Trans Neural Netw 21(3):520–526. doi:10.1109/TNN.2010.2040200

    Article  Google Scholar 

  11. Ultsch A, Siemon HP (1990) Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of international neural networks conference (INNC), pp 305–308. Paris http://www.uni-marburg.de/fb12/datenbionik/pdf/pubs/1990/UltschSiemon90

  12. Vesanto J (1999) Som-based data visualization methods. Intell Data Anal 3(2):111–126. doi:10.1016/S1088-467X(99)00013-X

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Vislab, School of I.T., The University of Sydney, Sydney, NSW, Australia

    Florence Y. Wang & Masahiro Takatsuka

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  1. Florence Y. Wang
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  2. Masahiro Takatsuka
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Correspondence to Florence Y. Wang.

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Wang, F.Y., Takatsuka, M. Self-organizing Map (SOM) Based Data Navigation for Identifying Shape Similarities of Graphic Logos. Neural Process Lett 41, 325–339 (2015). https://doi.org/10.1007/s11063-014-9375-4

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