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Emergent self-organizing feature map for recognizing road sign images

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Abstract

Road sign recognition system remains a challenging part of designing an Intelligent Driving Support System. While there exist many approaches to classify road signs, none have adopted an unsupervised approach. This paper proposes a way of Self-Organizing feature mapping for recognizing a road sign. The emergent self-organizing map (ESOM) is employed for the feature mapping in this study. It has the capability of visualizing the distance structures as well as the density structure of high-dimensional data sets, in which the ESOM is suitable to detect non-trivial cluster structures. This paper discusses the usage of ESOM for road sign detection and classification. The benchmarking against some other commonly used classifiers was performed. The results demonstrate that the ESOM approach outperforms the others in conducting the same simulations of the road sign recognition. We further demonstrate that the result obtained with ESOM is significantly more superior than traditional SOM which does not take into the boundary effect like ESOM did.

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References

  1. Kohonen T (1990) Self-organizing map. Proc IEEE 78(9):1464–1480

    Article  Google Scholar 

  2. Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, Berlin

    MATH  Google Scholar 

  3. Chang C-H, Xu P, Xiao R, Srikanthan T (2005) New adaptive color quantization method based on self-organizing maps. IEEE Trans Neural Networks 16(1):237–249

    Article  Google Scholar 

  4. Hu W, Xie D, Tan T (2004) A hierarchical self-organizing approach for learning the patterns of motion trajectories. IEEE Trans Neural Networks 15(1):135–144

    Article  Google Scholar 

  5. Wu S, Rahman MKM, Chow TWS (2005) Content-based image retrieval using growing hierarchical self-organizing quadtree map. Pattern Recogn 38(5):707–722

    Article  MATH  Google Scholar 

  6. Ritter H, Martinetz T, Schulten K (1992) Neural computation and self-organizing maps: an introduction. Addison-Wesley, Reading

    MATH  Google Scholar 

  7. Mulier F, Cherkassky V (1995) Self-organization as an iterative kernel smoothing process. Neural Comput 7:1165–1177

    Article  Google Scholar 

  8. Ultsch A, Siemon HP (1990) Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of the International Joint Conf Neural Networks, Paris, France, pp 305–308

  9. Pal NR, Eluri VK (1998) Two efficient connectionist schemes for structure preserving dimensionality reduction. IEEE Trans Neural Networks 9(6):1142–1154

    Article  Google Scholar 

  10. Kong A (2000) Interactive visualization and analysis of hierarchical neural projections for data mining. IEEE Trans Neural Networks 11(3):615–624

    Article  Google Scholar 

  11. Wu S, Chow TWS (2005) PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map. IEEE Trans Neural Networks 16(6):1362–1380

    Article  Google Scholar 

  12. Yin H (2002) ViSOM: a novel method for multivariate data projection and structure visualization. IEEE Trans Neural Networks 13(1):237–243

    Article  Google Scholar 

  13. Ultsch A, Mörchen F (2005) ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM”, Technical Report No. 46, Department of Mathematics and Computer Science, University of Marburg, Germany

  14. de la Escalera A, Armingol JM, Mata M (2003) Traffic sign recognition and analysis for intelligent vehicles. Image Vision Comput 21:247–258

    Article  Google Scholar 

  15. Vitabile S, Pollaccia G, Pilato G, Sorbello F (2001) Road signs recognition using a dynamic pixel aggregation technique in the HSV color space. In: Proceedings of the 11th international conference on image analysis and processing, Palermo, Italy, pp 572–577

  16. Paclik P, Novovicova J (2000) Road sign classification without colour information. In: Proceedings of 6th conference of advanced school of imaging and computing, ASCI, Lommel, Belgium

  17. Shaposhnikov D, Podladchikova LN, Golovan AV, Shevtsova N, Kunbin AH, Xiaohong G (2002) Road sign recognition by single positioning of space-variant sensor window. In Proceedings of 15th international conference on vision interface, Canada, Calgary, pp 213–217

  18. Nguwi Y, Kouzani AZ (2008) Detection and classification of road signs in natural environments. Neural Comput Appl 17(3):265–289

    Article  Google Scholar 

  19. Aoyagi Y, Asakura T (1996) A study on traffic sign recognition in scene image using genetic algorithms and neural networks. In: Proceedings of the 22nd international conference on industrial electronics, control, and instrumentation, Taipeis, pp 1838–1843

  20. Torresen J, Bakke J, Sekanina L (2004) Efficient recognition of speed limit signs. In: Proceedings of the 7th international IEEE conference on intelligent transportation systems, pp 652–656

  21. Ultsch A, Siemon H. (1990). Kohonen’s self organizing maps for exploratory data analysis. In: Proceedings of international neural network conference (INNC’90), pp 305–308

  22. Ultsch A (2003) Maps for the visualization of high-dimensional data spaces. In: Proceedings of the workshop on self organizing maps, Kyushu, Japan

  23. Ultsch A (2003) Pareto density estimation: a density estimation for knowledge Discovery. In: Baier D, Wernecke KD (eds) Innovations in classification, data science, and information systems—proceedings 27th annual conference of the german classification society (GfKL) 2003, Springer, Berlin, Heidelberg, pp 91–100

  24. Luc V, Soille P (1991) Watersheds in digital space: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–598

    Article  Google Scholar 

  25. La Cara GE, Ursino M (2004) A neural network model of contours extraction based on orientation selectivity in the primary visual cortex: applications on real images. In: Proceedings of 26th Annual International Conference of the IEEE EMBS

  26. Jones JP, Palmer LA (1987) An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6):1233–1258

    Google Scholar 

  27. La Cara GE, Ursino M, Bettini M (2003) Extraction of salient contours in primary visual cortex: a neural network model based on physiological knowledge. Proceedings of the 25th Annual International Conference of the IEEE 3:2242–2245

  28. Ultsch A (2003) U*-Matrix: a tool to visualize clusters in high dimensional data. Department of Computer Science University of Marburg, research report 36

  29. Ultsch A (2005) Clustering with SOM: U*C, WSOM 2005, Paris, pp 75–82

  30. Soetedjo A, Yamada K (2005) Traffic sign classification using ring partitioned method. IEICE Trans Fundamentals E88A(9):166–178

    Google Scholar 

  31. de la Escalera A, Radeva P (2004) Fast greyscale road sign model matching and recognition. In: Vitria J (ed) Recent advances in artificial intelligence research and development. IOS Press, Amsterdam, pp 69–76

    Google Scholar 

  32. Estevez L, Kehtarnavaz N (1996) A real-time histographic approach to road sign recognition. In: Proceedings of the IEEE southwest symposium on image analysis and interpretation, pp 95–100

  33. Gao X, Shevtsova N, Hong K, Batty S, Podladchikova L, Golovan A, Shaposhnikov D, Gusakova V (2002) Vision models based identification of traffic signs. In: Proceedings of the 1st European conference on color in graphics, image and vision, France, pp 47–51

  34. Vitabile S, Gentile A, Sorbello F (2002) A neural network based automatic road signs recognizer. Proc 2002 Int Joint Conf Neural Networks 3:2315–2320

    Google Scholar 

  35. Paclik P, Novovicova J, Pudil P, Somol P (2000) Road sign classification using the Laplace kernel classifier. Pattern Recogn Lett 21(13–14):1165–1173

    Article  MATH  Google Scholar 

  36. Gavrila DM, Philomin V. (1999) Real-time object detection for smart vehicles. In: Proceedings of IEEE international conference on computer vision, Greece, pp 87–93

  37. Ruta A, Li Y, Liu X (2010) Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recogn 43(1):416–430

    Article  MATH  Google Scholar 

  38. Miguel S, Prieto MS, Allen AR (2009) Using self-organising maps in the detection and recognition of road signs. Image Vis Comput 27(6, 4):673–683

    Google Scholar 

  39. Paclik P, Novovicova J, Duin R (2006) Building road-sign classifiers using a trainable similarity measure. IEEE Trans Intell Transp Syst 7(3):309–321

    Article  Google Scholar 

  40. Lalonde M, Li Y (1995) Road sign recognition, survey of the state of the art, Technical report, Centre de Recherche Informatique de Montreal CRIM/IIT

  41. Johansson B (2002) Road sign recognition from a moving vehicle, Master’s thesis, Centre for Image Analysis, Uppsala University

  42. Fleyeh H, Gilani S, Dougherty M (2006) Road sign detection and recognition using Fuzzy ARTMAP: a case study swedish speed-limit signs. In: Proceedings of Artificial Intelligence and Soft Computing

  43. Paclik P, Novovicova J, Pudil P, Somol P (2000) Road sign classification using the Laplace kernel classifier. Pattern Recogn Lett 21(13–14):1165–1173

    Article  MATH  Google Scholar 

  44. Fang CY, Chen SW, Fuh CS (2003) Road-sign detection and tracking. IEEE Trans Vehicular Technol 52:1329–1341

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Computing Systems, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Yok-Yen Nguwi & Siu-Yeung Cho

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  1. Yok-Yen Nguwi
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  2. Siu-Yeung Cho
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Correspondence to Siu-Yeung Cho.

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Nguwi, YY., Cho, SY. Emergent self-organizing feature map for recognizing road sign images. Neural Comput & Applic 19, 601–615 (2010). https://doi.org/10.1007/s00521-009-0315-6

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  • DOI: https://doi.org/10.1007/s00521-009-0315-6

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