Abstract
In this paper we propose a new approach for pattern recognition problems with non-uniform classes of images. The main idea of this classification method is to describe classes of images with their fuzzy portraits. This approach provides good generalizing ability of algorithm. The fuzzy set is calculated as a preliminary result of algorithm before crisp decision or rejecting that allows to solve a problem of uncertainly at the boundaries of classes. We use the method to solve the problem of knife detection in still images. The main idea of this study is to test fuzzy classification with features vectors in real environment. As a feature vectors we decided to use selected MPEG-7 descriptors schemes. The described method was experimentally validated on dataset with over 12 thousands images. The article contains results of five experiments which confirm good accuracy of the proposed method.
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References
Ć»ywicki, M., MatiolaĆski, A., Orzechowski, T.M., Dziech, A.: Knife detection as a subset of object detection approach based on Haar cascades. In: Proceedings of 11th International Conference on Pattern Recognition and Information Processing, Minsk, Belarus, pp. 139â142 (2011)
Glowacz, A., KmieÄ, M., Dziech, A.: Visual Detection of Knives in Security Applications using Active Appearance Models. Multimedia Tools and Applications (2013)
Maksimova, A.: Knife Detection Scheme Based on Possibilistic Shell Clustering. In: Dziech, A., CzyĆŒewski, A. (eds.) MCSS 2013. CCIS, vol. 368, pp. 144â152. Springer, Heidelberg (2013)
Konor, A.: Computational Intelligence: Principles, Techniques and Applications. Springer, Heidelberg (2005)
Kuncheva, L.I.: Fuzzy Classifier Design. Physica-Verlag, Heidelberg (2005)
Ishibuchi, H., Nakashima, T., Nii, M.: Classification and Modeling with Linguistic Information Granules. Springer (2005)
Chen, N.: Fuzzy Classification Using Self-Organizing Map and Learning Vector Quantization. In: Shi, Y., Xu, W., Chen, Z. (eds.) CASDMKM 2004. LNCS (LNAI), vol. 3327, pp. 41â50. Springer, Heidelberg (2005)
Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer, New York (2005)
Baran, R., Glowacz, A., Matiolanski, A.: The efficient real- and non-real-time make and model recognition of cars. Multimedia Tools and Applications (2013)
Information Won, C.S., Park, D.K., Park, S.-J.: Efficient Use of MPEG-7 Edge Histogramm. ETRI J. 24(1), 23â30 (2002)
Ro, Y.M., Kim, M., Kang, H.K., Manjunath, B.S., Kim, J.: MPEG-7 Homogeneous Texture Descriptor. ETRI Journal 23(2), 41â51 (2001)
Yu, M.A., Kozlovskii, V.A.: Algorithm of Pattern Recognition with intra-class clustering. In: Proceedings of 11th International Conference on Pattern Recognition and Processing, Minsk, pp. 54â57 (2011)
Pal, N.R., Bezdek, J.C.: On Cluster Validity for the Fuzzy c-Means Model. J. IEEE Transactions on Fuzzy Systems 3(3), 370â379 (1995)
Maksimova, A.: Decision Making Method for Classifying Models Based on Intra-class Clustering on FCM-algorithm. Artificial Intelligent J. 3(61), 171â178 (2013) (in Russian)
Maksimova, A.: The Model of Data Presentation with Fuzzy Portraits for Pattern Recognition. Int. J. of Computing 11(1), 17â24 (1995)
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Maksimova, A., MatiolaĆski, A., Wassermann, J. (2014). Fuzzy Classification Method for Knife Detection Problem. In: Dziech, A., CzyĆŒewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2014. Communications in Computer and Information Science, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-319-07569-3_13
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DOI: https://doi.org/10.1007/978-3-319-07569-3_13
Publisher Name: Springer, Cham
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