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.
Similar content being viewed by others
References
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
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
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
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
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
Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480
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
MPEG-7: Shape dataset. http://www.cis.temple.edu/latecki/TestData/mpeg7shapeB.tar.gz
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
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
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
Vesanto J (1999) Som-based data visualization methods. Intell Data Anal 3(2):111–126. doi:10.1016/S1088-467X(99)00013-X
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-014-9375-4