Abstract
This article addressed some extensions of a texture classifier invariant to rotations. Originally, that classifier is an improvement of the seminal Haralick’s paper in a sense that the former is rotation invariant due to a circular kernel, which encompasses two concentric circles with different radii and then the co-occurrence matrix is formed. It is not considered only pixels falling exactly on the circle, but also others in its vicinity according to a Gaussian scattering. Firstly, 6 attributes are computed from each of the 18 texture patterns, after that texture patterns are rotated and a correct classification, considering Euclidian distance, is sought. The present paper assesses the performance of the afore-mentioned approach with some alterations: Tsallis rather than Gaussian distribution; addition of noise to rotated images before classification; and Principal Components Analysis during the extraction of features.
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References
Brandt, T., Mather, P.M.: Classification Methods for Remotely Sensed Data. CRC Press (2009)
Acunã, M.A.B.: Rotation-Invariant Texture Classification Based on Grey Level Co-occurrence Matrices. Universidade de São Paulo (2013)
Haralick, R.M., Shanmugam, M., Dinstein, I.: Texture feature for image classification. IEEE Transactions on Systems, Man and Cybernetics 3, 610–621 (1973)
Liu, L., Long, Y., Fieguth, P.W., Lao, S., Zhao, G.: BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification. IEEE Transactions on Image Processing 23(7), 3071–3084 (2014)
Hassan, A., Riaz, F., Shaukat, A.: Scale and Rotation Invariant Texture Classification Using Covariate Shift Methodology. IEEE Signal Processing Letters 21(3), 321–324 (2014)
Ito, R.H., Kim, H.Y., Salcedo, W.J.: Classificação de Texturas Invariante a Rotação Usando Matriz de Co-ocorrência. In: 8th International Information and Telecommunication Technologies Symposium (2009)
Baraldi, A., Parmiggiani, F.: An Investigation of the Textural Characteristics Associated with Gray Level Co-occurrence Matrix Statistical Parameters. IEEE Transactions on Geoscience and Remote Sensing 33(2), 293–304 (1995)
Tsallis, C.: Possible generalization of boltzmann-gibbs statistics. Journal Statistical Physics 52, 479 (1988)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)
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© 2015 Springer International Publishing Switzerland
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Habermann, M., Campos, F.B., Shiguemori, E.H. (2015). Rotation Invariant Texture Analysis Based on Co-occurrence Matrix and Tsallis Distribution. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_24
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DOI: https://doi.org/10.1007/978-3-319-20469-7_24
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