Abstract
In this paper, we propose a novel texture analysis method using the complex network theory. It was investigated how a texture image can be effectively represented, characterized and analyzed in terms of a complex network. The propose uses degree measurements in a dynamic evolution network to compose a set of feasible shape descriptors. Results show that the method is very robust and it presents a very excellent texture discrimination for all considered classes.
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Antiqueira, L., Nunes, M.G.V., Oliveira, O.N., Costa, L.F.: Strong correlations between text quality and complex networks features. Physica A 373(1), 811–820 (2007)
Azencott, R., Wang, J.P., Younes, L.: Texture classification using windowed fourier filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2), 148–153 (1997)
Backes, A.R., Bruno, O.M.: Shape classification using complex network and multi-scale fractal dimension. Pattern Recognition Letters 31, 44–51 (2010)
Backes, A.R., Casanova, D., Bruno, O.M.: A complex network-based approach for boundary shape analysis. Pattern Recognition 42(1), 54–67 (2009)
Brodatz, P.: Textures: A photographic album for artists and designers. Dover Publications, New York (1966), http://www.ux.uis.no/~tranden/brodatz.html
Chalumeau, T., Costa, L.F., Laligant, O., Meriaudeau, F.: Texture discrimination using hierarchical complex networks. In: Proceedings of the Second International Conference on Signal-Image Technology and Internet-Based Systems, pp. 543–550 (2006)
Chalumeau, T., da Costa, L.F., Laligant, O., Meriaudeau, F.: Complex networks: application for texture characterization and classification. Electronic Letters on Computer Vision and Image Analysis 7(3), 93–100 (2008)
Chalumeau, T., da Costa, L.F., Laligant, O., Meriaudeau, F.: Texture discrimination using hierarchical complex networks. Multimedia Systems and Applications 31(2), 95–102 (2008)
Costa, L.F.: Complex networks, simple vision (2004), http://arxiv.org/abs/cond-mat/0403346
Costa, L.F., Rodrigues, F.A., Travieso, G., Boas, P.R.V.: Characterization of complex networks: A survey of measurements. Advances in Physics 56(1), 167–242 (2007)
Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of IEEE 67(5), 786–804 (1979)
Julesz, B.: Experiments in the visual perception of texture. Scientific American 232(4), 34–43 (1975)
Kasparis, T., Charalampidis, D., Georgiopoulos, M., Rolland, J.P.: Segmentation of textured images based on fractals and image filtering. Pattern Recognition 34(10), 1963–1973 (2001)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)
Tuceryan, M., Jain, A.K.: Texture analysis. In: Handbook of Pattern Recognition and Computer Vision, pp. 235–276 (1993)
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Backes, A.R., Casanova, D., Bruno, O.M. (2010). A Complex Network-Based Approach for Texture Analysis. In: Bloch, I., Cesar, R.M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2010. Lecture Notes in Computer Science, vol 6419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16687-7_48
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DOI: https://doi.org/10.1007/978-3-642-16687-7_48
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