Block Texture Pattern Detection Based on Smoothness and Complexity of Neighborhood Pixels

Full Text (PDF, 758KB), PP.1-9

Views: 0 Downloads: 0


Amir Farhad Nilizadeh 1,* Ahmad Reza Naghsh Nilchi 2

1. Department of Computer Engineering, Arak Branch, Islamic Azad University, Arak, Iran

2. Department of Artificial Intelligence and Multimedia Engineering, University of Isfahan, Iran

* Corresponding author.


Received: 8 Jan. 2014 / Revised: 11 Feb. 2014 / Accepted: 6 Mar. 2014 / Published: 8 Apr. 2014

Index Terms

Image classification, Texture analysis, Block texture pattern, Texture complexity, Data hiding, LSB, PVD, Matrix pattern (MP)


In this paper, a novel method for detecting Block Texture Patterns (BTP), based on two measures: smoothness and complexity of neighborhood pixels is proposed. With these two measures, a new classification for texture detection is defined. Texture detection with these measures can be used in many image processing and computer vision applications. As an example, the applicability of BTP on data hiding algorithms is discussed, and the advantages of this classification on these algorithms are shown.

Cite This Paper

Amir Farhad Nilizadeh, Ahmad Reza Naghsh Nilchi,"Block Texture Pattern Detection Based on Smoothness and Complexity of Neighborhood Pixels", IJIGSP, vol.6, no.5, pp.1-9, 2014. DOI: 10.5815/ijigsp.2014.05.01


[1]S. Arivazhagan, S. S. Nidhyanandhan and R. N. Shebiah, “Texture categorization using statistical and spectral features”, International Conference on Computing, Communication and Networking, pp. 1-9, 2008. 

[2]A. Fathi and A. R. Naghsh Nilchi, “Noise tolerant local binary pattern operator for efficient texture analysis”, Pattern Recognition Letters, 33.9, pp. 1093-1100, 2012.

[3]R. M. Haralick, K. Shunmugam and I. Dinstein, “Textural Features for Image Classification”, IEEE Transactions on Systems, Man and Cybernetics, 3.6, pp. 610-621, 1973.

[4]B. V. Reddy, M. R. Mani and K. V. Subbaiah, “Texture classification method using Wavelet transforms based on Gaussian Markov Random Field”, International Journal of Signal and Image Processing, 1.1, pp. 35-39, 2010.

[5]R. Khelifi, M. Adel and S. Bourennane, “Texture classification for multi-spectral images using spatial and spectral gray level differences”, IEEE 2nd International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 330-333, 2010.

[6]K. I. Laws, “Textured Image Segmentation”, No. USCIPI-940, University of Southern California Los Angeles Image Processing INST, 1980.

[7]A. Sengur, “Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification”, Expert Systems with Applications, 34.3, pp. 2120-2128, 2008.

[8]Y. Venkateswarlu, B. Sujatha, J. V. R. Murthy, “A New Approach for Texture Classification Based on Average Fuzzy Left Right Texture Unit Approach”, International Journal Image, Graphics and Signal Processing (IJIGSP), Vol. 4, No. 12, pp. 57-64, 2012.

[9]T. Celik and T. Tjahjadi, “Multiscale texture classification using dual-tree complex wavelet transform”, Pattern Recognition Letters, 30.3, pp. 331-339, 2009.

[10]D. Choudhary, A. K. Singh, S. Tiwari and V. P. Shukla, “Performance Analysis of Texture Image Classification Using Wavelet Feature”, International Journal Image, Graphics and Signal Processing (IJIGSP), 5.1, pp. 58-63, 2013.

[11]S. Arivazhagan, L. Ganesan and S. P. Priyal, “Texture classification using Gabor wavelets based rotation invariant features”, Pattern Recognition Letters, 27.16, pp. 1976-1982, 2006.

[12]T. Randen and J. H.Husoy, “Filtering for Texture Classification: A Comparative Study”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21.4, pp. 291-310, 1999.

[13]M. Varma and R. Garg, “Locally invariant fractal features for statistical texture classification”, IEEE 11th International Conference on Computer Vision, pp. 1-8, 2007.

[14]R. A. Peters and R. N. Strickland, “Image complexity metrics for automatic target recognizers”, Automatic Target Recognizer System and Technology Conference, pp. 1-17, 1990.

[15]J. Perkiö and A. Hyvärinen, “Modelling image complexity by independent component analysis, with application to content-based image retrieval”, In Artificial Neural Networks–ICANN, Springer Berlin Heidelberg, pp. 704-714, 2009.

[16]N. F. Johnson and S. Jajodia, “Exploring steganography: Seeing the unseen”, IEEE on computer, 31.2, pp. 26-34, 1998.

[17]R. Proulx and L. Parrott, “Measures of structural complexity in digital images for monitoring the ecological signature of an old-growth forest ecosystem”, Ecological Indicators, 8.3, pp. 270-284, 2008.

[18]C. Liu, R. Szeliski, S. B. Kang, C. L. Zitnick and W. T. Freeman, “Automatic estimation and removal of noise from a single image”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30.2, pp. 299-314, 2008.

[19]R. Chandramouli and N. Memon, “Analysis of LSB based image steganography techniques”, IEEE International Conference on Image Processing, Vol. 3, pp. 1019-1022, 2001.

[20]X. Zhang and S. Wang, “Vulnerability of pixel-value differencing steganography to histogram analysis and modification for enhanced security”, Pattern Recognition Letters, 25.3, pp. 331-339, 2004.

[21]A. F. Nilizadeh and A. R. Naghsh Nilchi, “Steganography on RGB Images Based on a "Matrix Pattern" using Random Blocks”, International Journal of Modern Education and Computer Science (IJMECS), 5.4, pp. 8-18, 2013.

[22]S. Sahu, S. k. Nanda and T. Mohapatra, “Digital Image Texture Classification and Detection Using Radon Transform”, International Journal Image, Graphics and Signal Processing (IJIGSP), 5.12, pp. 38-48, 2013.

[23]E. Kawaguchi and R. O. Eason, “Principles and applications of BPCS steganography”, In Photonics East (ISAM, VVDC, IEMB), International Society for Optics and Photonics, pp. 464-473, 1999.

[24]G. N. Srinivasan and G. Shobha, “Statistical texture analysis”, proceedings of world academy of science: Engineering & Technology, 48, 2008.

[25]O. Holub and S. T. Ferreira, “Quantitative histogram analysis of images”, Computer physics communications, 175.9, pp. 620-623, 2006.

[26]T. S. Sazzad, M. Z. Hasan and F. Mohammed, “Gamma encoding on image processing considering human visualization, analysis and comparison”, International Journal on Computer Science & Engineering, 4.12, 2012.

[27]Proving that 1+2+3+...+n is n(n+1)/2, Retrieved Oct 2013, from

[28]C. M. Wang, N. I. Wu, C. S. Tsai and M. S. Hwang, “A high quality steganographic method with pixel-value differencing and modulus function”, Journal of Systems and Software, 81.1, pp. 150-158, 2008.

[29]K. J. Kim, K. H. Jung and K. Y. Yoo, “A high capacity data hiding method using PVD and LSB”, IEEE Computer Society In Proceedings of the International Conference on Computer Science and Software Engineering, Volume 03, pp. 876-879, 2008.

[30]M. Gadiparthi, K. Sagar, D. Sahukari and R. Chowdary, “A High Capacity Steganographic Technique based on LSB and PVD Modulus Methods”, International Journal of Computer Applications, 22.5, pp. 8-11, 2011.

[31]H. C. Wu, N. I. Wu, C. S. Tsai and M. S. Hwang, “Image steganographic scheme based on pixel-value differencing and LSB replacement methods”, IEE Proceedings-Vision, Image and Signal Processing 152.5, pp. 611-615, 2005.

[32]J. Y. Hsiao, K. F. Chan and J. Morris Chang, “Block-based reversible data embedding”, Signal Processing, 89.4, pp. 556-569, 2009.