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A hybrid approach for texture classification based on complex network | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 17، دوره 14، شماره 7، مهر 2023، صفحه 189-195 اصل مقاله (662.86 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.28443.3888 | ||
| نویسندگان | ||
| Ali Ebrahimi1؛ Kamal Mirzaie* 1؛ Ali Mohamad Latif2 | ||
| 1Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran | ||
| 2Department of Computer Engineering, Yazd University, Yazd, Iran | ||
| تاریخ دریافت: 26 تیر 1401، تاریخ بازنگری: 29 مرداد 1401، تاریخ پذیرش: 27 شهریور 1401 | ||
| چکیده | ||
| This paper proposes a method for image texture classification based on a complex small world network model. Finding important and valuable information in the context of an image is a big problem for image classification. In current analysis methods, image texture features such as spatial information are left out and color histogram is mostly used. In this article, the multi-radial distance analysis method is used to select the nodes, and then based on the identified nodes and calculating the shortest distance between adjacent nodes, a complex network is created to record the texture pattern. In the next step, the topological characteristics of the network such as the number of nodes, the number of triple triangles, the length of the edges and the average diameter of the network are identified and used for evaluation. Brodatz, UIUC and Outex databases have been used to test the system. The results of the proposed method show that this method is effective for texture classification and improves the classification rate compared to the usual traditional methods. | ||
| کلیدواژهها | ||
| Texture analysis؛ Texture classification؛ Dynamic complex network؛ Network similarity | ||
| مراجع | ||
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[1] T. Acharya and A. K. Ray, Image processing: Principles and applications, Wiley-Interscience Publishers, 2005. [2] R. Azencott, J. Wang and L. Younes, Texture classification using windowed Fourier filters, IEEE Trans. Pattern Anal. Machine Intell. 19 (1997), no. 2, 148–153. [3] A.R. Backes, D. Casanova and O.M. Bruno, Texture analysis and classification: A network based approach, Inf. Sci. 219 (2015), 168–180. [4] P. Brodatz, Textures: A photographic album for artists and designers, Dover Publications, New York, 1966. [5] M. Idrissa and M. Acheroy, Texture classification using Gabor filters, Pattern Recog. Lett. 23 (2002), no. 9, 1095–1102. [6] J.J.D.M.S. Junior, P.C. Cortez and A.R. Backes, Texture analysis and classification using shortest paths in graphs, Pattern Recog. Lett. 34 (2013), no. 11, 1314–1319. [7] S. Lazebnik, C. Schmid and J. Ponce, A sparse texture representation using local affine regions, IEEE Trans. Pattern Anal. Machine Intell. 27 (2005), no. 8, 1265–1278. [8] A. Materka and M. Strzelecki, Texture analysis methods –A review, Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 10 (1998), no. 1.97, 4968. [9] M.E.J. Newman, The structure and function of complex networks, SIAM Rev. 45 (2003), 167–256. [10] T. Ojala, T. Maenpaa, M. Pietikainen, J. Viertola, J. Kyllonen and S. Huovinen, Outex – New framework for empirical evaluation of texture analysis algorithms, Object Recogn. Supported User Interact. Service Robots 1 (2002), 701–706. [11] T. Ojala, M. Pietikainen and D. Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recog. 29 (1996), no. 1, 51–59. [12] T. Ojala, M. Pietikainen and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Machine Intell. 24 (2002), no. 7, 971–987. [13] L.F.S. Scabini, W.N. Goncalves and A.A. Castro, Texture analysis by bag-of-visual-words of complex networks, Proc. of the 20th Iberoamerican Cong. (CIARP), Montevideo, Uruguay, 2018, pp. 485–492. | ||
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