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Image Indexing and Retrieval Using GSOM Algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

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Abstract

Growing Self Organized Map (GSOM) algorithm is a well-known unsupervised clustering algorithm which a definite advantage is that both the map structure as well as the number of classes are automatically adjusted depending on the training data. We propose a new approach to apply it in the process of the image indexation and retrieval in a database. Unlike the classic bag-of-words (BoW) algorithm with k-means clustering, it is completely unnecessary to predetermine the number of classes (words). Thanks to that, the process of indexation can be fully automated. What is more, numerous modifications of the classic algorithm were added, and as a result, the retrieval process was considerably improved. Results of the experiments as well as comparison with BoW are presented at the end of the paper.

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References

  1. Audet, S.: JavaCV (2014), http://bytedeco.org/ (Online; accessed December 1, 2014)

  2. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006), http://dx.doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  3. Biniaz, A., Abbasi, A.: Fast FCM with spatial neighborhood information for brain mr image segmentation. Journal of Artificial Intelligence and Soft Computing Research 3(1), 15–25 (2014)

    Google Scholar 

  4. Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)

    Google Scholar 

  5. Chen, M., Ludwig, S.: Particle swarm optimization based fuzzy clustering approach to identify optimal number of clusters. Journal of Artificial Intelligence and Soft Computing Research 4(1), 43–56 (2014)

    Article  Google Scholar 

  6. Cpalka, K., Rutkowski, L.: Flexible takagi-sugeno fuzzy systems. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 3, pp. 1764–1769 (July 2005)

    Google Scholar 

  7. Cpalka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Analysis: Theory, Methods & Applications 71(12), e1659 – e1672 (2009), http://www.sciencedirect.com/science/article/pii/S0362546X09002831

  8. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)

    Google Scholar 

  9. Dittenbach, M., Merkl, D., Rauber, A.: The growing hierarchical self-organizing map. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 6, p. 6015. IEEE Computer Society (2000)

    Google Scholar 

  10. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2004, p. 178 (June 2004)

    Google Scholar 

  11. Fritzke, B.: Growing grid – a self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2(5), 9–13 (1995), http://dx.doi.org/10.1007/BF02332159

    Article  Google Scholar 

  12. Koshiyama, A.S., Vellasco, M.M.B.R., Tanscheit, R.: Gpfis-control: A genetic fuzzy system for control tasks. Journal of Artificial Intelligence and Soft Computing Research 4(3) (March 2015)

    Google Scholar 

  13. Łapa, K., Przybył, A., Cpałka, K.: A new approach to designing interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS (LNAI), vol. 7895, pp. 523–534. Springer, Heidelberg (2013), http://dx.doi.org/10.1007/978-3-642-38610-7_48

    Chapter  Google Scholar 

  14. Liu, J.: Image retrieval based on bag-of-words model. CoRR abs/1304.5168 (2013), http://arxiv.org/abs/1304.5168

  15. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  MATH  Google Scholar 

  16. Ludwig, S.: Repulsive self-adaptive acceleration particle swarm optimization approach. Journal of Artificial Intelligence and Soft Computing Research 4(3), 189–204 (2015)

    MathSciNet  MATH  Google Scholar 

  17. Najgebauer, P., Nowak, T., Romanowski, J., Rygał, J., Korytkowski, M., Scherer, R.: Novel method for parasite detection in microscopic samples. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 551–558. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  18. Nowicki, R.: Rough–neuro–fuzzy system with MICOG defuzzification. In: Proceedings of IEEE International Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, pp. 1958–1965 (July 2006)

    Google Scholar 

  19. Nowicki, R.: Nonlinear modelling and classification based on the MICOG defuzzification. Journal of Nonlinear Analysis, Series A: Theory, Methods & Applications 7(12), e1033–e1047 (2009)

    Google Scholar 

  20. Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)

    Article  Google Scholar 

  21. Starczewski, J., Scherer, R., Korytkowski, M., Nowicki, R.: Modular type-2 neuro-fuzzy systems. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 570–578. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Woźniak, M., Kempa, W.M., Gabryel, M., Nowicki, R.K.: A finite-buffer queue with single vacation policy - analytical study with evolutionary positioning. International Journal of Applied Mathematics and Computer Science 24(4), 887–900 (2014)

    MathSciNet  Google Scholar 

  23. Zhu, G., Zhu, X.: The growing self-organizing map for clustering algorithms in programming codes. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI), vol. 3, pp. 178–182 (October 2010)

    Google Scholar 

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Correspondence to Marcin Gabryel .

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Gabryel, M., Grycuk, R., Korytkowski, M., Holotyak, T. (2015). Image Indexing and Retrieval Using GSOM Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_63

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_63

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

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