INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.10, No.9, Sep. 2018

Galois Field-based Approach for Rotation and Scale Invariant Texture Classification

Full Text (PDF, 1009KB), PP.56-64


Views:27   Downloads:1

Author(s)

Shivashankar S., Medha Kudari, Prakash S. Hiremath

Index Terms

Galois Field representation of texture image;Feature histogram computation; Rotation and scale invariance;Texture classification

Abstract

In this paper, a novel Galois Field-based approach is proposed for rotation and scale invariant texture classification. The commutative and associative properties of Galois Field addition operator are useful for accomplishing the rotation and scale invariance of texture representation. Firstly, the Galois field operator is constructed, which is applied to the input textural image. The normalized cumulative histogram is constructed for Galois Field operated image. The bin values of the histogram are considered as rotation and scale invariant texture features. The classification is performed using the K-Nearest Neighbour classifier. The experimental results of the proposed method are compared with that of Rotation Invariant Local Binary Pattern (RILBP) and Log-Polar transform methods. These results obtained using the proposed method are encouraging and show the possibility of classifying texture successfully irrespective of its rotation and scale.

Cite This Paper

Shivashankar S., Medha Kudari, Prakash S. Hiremath, " Galois Field-based Approach for Rotation and Scale Invariant Texture Classification", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.9, pp. 56-64, 2018.DOI: 10.5815/ijigsp.2018.09.07

Reference

[1]S. Purohit and S. R. Gandhi, “Application of Sparse Coded SIFT Features for Classification of Plant Images”. International Journal of Image, Graphics and Signal Processing, vol 9(10), pp. 50-59, 2017. “doi: 10.5815/ijigsp.2017.10.06 “ 

[2]K.S. Reddy, V.V. Kumar and B.E. Reddy, “Face recognition based on texture features using local ternary patterns”, International Journal of Image, Graphics and Signal Processing, vol 7(10),pp. 37-46,  2015. “doi: 10.5815/ijigsp.2015.10.05“

[3]T.P. Nguyen, N.S. Vu, and A. Manzanera, “Statistical binary patterns for Rotational Invariant Texture Classification”, Neurocomputing, vol. 173, pp. 1565-1577, 2016.”doi:10.1016/j.neucom.2015.09.029” 

[4]R.K. Goyal, W.L. Goh, D.P. Mital and K.L. Chan, “Scale and Rotation Invariant Texture Analysis based on Structural property”, in Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on, Vol. 2, pp. 1290-1294, IEEE, November, 1995. ”doi:  10.1109/IECON.1995.483983”

[5]Y.Q. Chen, M.S. Nixon and D.W. Thomas, “Statistical Geometrical Features for Texture Classification”, Pattern Recognition, vol. 28(4), pp. 537-552, 1995. “doi: 10.1016/0031-3203(94)00116-4”, 

[6]C. Dharmagunawardhana, S. Mahmoodi, M. Bennett and M. Niranjan, “Rotation Invariant Texture Descriptors based on Gaussian Markov random fields for Classification”, Pattern Recognition Letters, vol. 69, pp. 15-21, 2016.”doi: 10.1016/j.patrec.2015.10.006”

[7]F. Bianconi and A. Fernández, “Rotation Invariant Co-occurrence features based on digital circles and Discrete Fourier Transform”, Pattern Recognition Letters, vol. 48, pp. 34-41, 2014.”doi: 10.1016/j.patrec.2014.04.006” 

[8]P. Simon and V. Uma, “Review of Texture Descriptors for Texture Classification”, In Data Engineering and Intelligent Computing pp. 159-176, Springer, Singapore, 2018. “doi: 10.1007/978-981-10-3223-3_15” 

[9]R.L. Kashyap and A. Khotanzad, “A model-based method for Rotation Invariant Texture Classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, pp. 472-481, 1986.”doi: 10.1109/TPAMI.1986.4767811”

[10]T. Ojala, M. Pietikainen and T. Maenpaa, “Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Transactions on pattern analysis and machine intelligence, vol. 24(7), pp. 971-987, 2002. “doi: 10.1109/TPAMI.2002.1017623”

[11]R. Porter and N. Canagarajah, “Robust Rotation-Invariant Texture Classification: wavelet, Gabor filter and GMRF based schemes”, IEEE Proceedings-Vision, Image and Signal Processing, vol. 144(3), pp. 180-188, 1997. “doi: 10.1049/ip-vis:19971182” 

[12]S. Arivazhagan, L. Ganesan and S.P. Priyal, “Texture classification using Gabor wavelets based Rotation Invariant features”, Pattern Recognition Letters vol. 27(16), pp. 1976-1982, 2006.”doi: 10.1016/j.patrec.2006.05.008”

[13]S. Bharkad and M. Kokare, “Rotation-invariant fingerprint matching using Radon and DCT”, Sadhana, vol. 42(12), pp. 2025-2039, 2017.”doi: 10.1007/s12046-017-0752-3”

[14]T. Song, H. Li, F. Meng, Q. Wu, and J. Cai, “Letrist: Locally Encoded Transform feature histogram for Rotation-Invariant Texture Classification”, IEEE Transactions on Circuits and Systems for Video Technology, 2017. “doi: 10.1109/TCSVT.2017.2671899”

[15]S.K. Roy, N. Bhattacharya, B. Chanda, B.B. Chaudhuri, and D.K. Ghosh, “FWLBP: A Scale Invariant Descriptor for Texture Classification”, arXiv preprint arXiv:1801.03228, 2018.

[16]Y. Xu, H. Ji, and C. Fermüller, “Viewpoint invariant texture description using Fractal Analysis”, International Journal of Computer Vision, vol. 83(1), pp. 85-100, 2009. “doi: 10.1007/s11263-009-0220-6”

[17]L. Liu, P. Fieguth, G. Kuang, and H. Zha, “Sorted random projections for robust Texture Classification”, In Computer Vision (ICCV), 2011 IEEE International Conference on,  pp. 391-398, IEEE, November, 2011.”doi: 10.1109/ICCV.2011.6126267” 

[18]J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid, “Local features and kernels for Classification of Texture and Object Categories: A comprehensive study”, International Journal of Computer Vision, vol. 73(2), pp. 213-238, 2007. “doi: 10.1007/s11263-006-9794-4”

[19]M. Crosier, and L.D. Griffin, “Using basic image features for Texture Classification”, International Journal of Computer Vision, vol. 88(3), pp. 447-460, 2010. “doi: 10.1007/s11263-009-0315-0”

[20]J. Zhang, and T. Tan, “Affine invariant Classification and Retrieval of Texture Images”, Pattern Recognition, vol. 36(3), pp. 657-664, 2003. “doi: 10.1016/S0031-3203(02)00099-7”

[21]Y. Quan, Y. Xu, and Y. Sun, “A distinct and compact texture descriptor”, Image and Vision Computing, vol. 32(4), pp. 250-259, 2014. “doi: 10.1016/j.imavis.2014.02.004”

[22]J. Mao, and A.K. Jain, “Texture classification and segmentation using Multiresolution Simultaneous Autoregressive Models”, Pattern Recognition, vol. 25(2), pp. 173-188, 1992. ”doi: 10.1016/0031-3203(92)90099-5”

[23]C.M. Pun, and M.C. Lee, “Log-polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification”, IEEE transactions on pattern analysis and machine intelligence, vol. 25(5), pp. 590-603, 2003. ”doi: 10.1109/TPAMI.2003.1195993” 

[24]J. Han, and K.K Ma, “Rotation-invariant and Scale-invariant Gabor features for texture image retrieval”, Image and vision computing, vol. 25(9), pp. 1474-1481, 2007. “doi: 10.1016/j.imavis.2006.12.015” 

[25]J. Zhang, and T. Tan, “Brief review of invariant texture analysis methods”, Pattern Recognition, vol. 35(3), pp. 735-747, 2002. 

[26]I.S. Reed, T.K. Truong, Y.S. Kwoh, and E.L. Hall, “Image Processing by Transforms over a Finite Field”, IEEE Transactions on Computers, vol. (9), pp. 874-881, 1977. ”doi: 10.1109/TC.1977.1674935”

[27]S. Shivashankar, M. Kudari and P.S. Hiremath,” Texture Representation Using Galois Field for Rotation Invariant Classification”. In Signal-Image Technology & Internet-Based Systems (SITIS), 2017 13th International Conference on. IEEE. pp. 237-240, Dec 2017. “doi: 10.1109/SITIS.2017.48”

[28]R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification (Vol. 2). New York: Wiley, 1973.

[29]P. Brodatz, Textures: a photographic album for artists and designers, Dover Publications, New York, USA, 1966.

[30]MondialMarmi database, Available online at http://dismac.dii.unipg.it/mm/ver_1_1/index.html

[31]OuTeX, OuTeX database, Available online at http://www.outex.oulu.fi/, 2002.

[32]All-free-download, Available online at http:// all-free-download.com, 2012.