IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 2    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 2,February 2012
A New Logical Compact LBP Co-Occurrence Matrix for Texture Analysis
Full Text(PDF, )  PP.119-123  
Author(s)
B. Sujatha, Dr.V.VijayaKumar, Dr.P. Harini
KEYWORDS
Texton, LBP, First and Second order statistical features, LCLBP-OR.
ABSTRACT
Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. Statistical and structural approaches have extensively studied in the texture analysis and classification whereas little work has reported to integrate them. One of the most popular statistical methods used to measure the textural information of images is the grey-level co-occurrence matrix (GLCM). The present paper combines the Logical Compact LBP with OR operator (LCLBP-OR), which is derived on textons, with GLCM approach and LCLBPCM using three stages. The LCLBP-OR reduces the texture unit size from 0 to 255 to 0 to 15 and achieves much better rotation invariant classification than conventional LBP. The LCLBP-OR values are obtained by applying the logical OR operator in between relative positions of LBP window. To evaluate micro texture features in stage one textons are evaluated. To make texture features relatively invariant with respect to changes in illumination and image rotation LCLBP-OR images are applied on LBP images of texton shapes in stage-two. Later in stage three the GLCM is constructed on LCLBP-OR and first and second order statistical features are evaluated for precise and accurate classification. The experimental results indicate the proposed LCLBPCM method classification performance is superior to that LBP, Gabor and other methods.
References
[1] J.S. Bridle, ―Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition,‖ Neurocomputing—Algorithms, Architectures and Applications, F. Fogelman-Soulie and J. Herault, eds., NATO ASI Series F68, Berlin: Springer-Verlag, pp. 227-236, 1989. (Book style with paper title and editor)

[2] W.-K. Chen, Linear Networks and Systems. Belmont, Calif.: Wadsworth, pp. 123-135, 1993. (Book style)

[3] H. Poor, ―A Hypertext History of Multiuser Dimensions,‖ MUD History, http://www.ccs.neu.edu/home/pb/mud-history.html. 1986. (URL link *include year)

[4] K. Elissa, ―An Overview of Decision Theory,"unpublished. (Unplublished manuscript)

[5] R. Nicole, "The Last Word on Decision Theory," J. Computer Vision, submitted for publication. (Pending publication)

[6] C. J. Kaufman, Rocky Mountain Research Laboratories, Boulder, Colo.,personal communication, 1992. (Personal communication)

[7] D.S. Coming and O.G. Staadt, "Velocity-Aligned Discrete Oriented Polytopes for Dynamic Collision Detection," IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 1, pp. 1-12, Jan/Feb 2008, doi:10.1109/TVCG.2007.70405. (IEEE Transactions )

[8] S.P. Bingulac, ―On the Compatibility of Adaptive Controllers,‖ Proc. Fourth Ann. Allerton Conf. Circuits and Systems Theory, pp. 8-16, 1994. (Conference proceedings)

[9] H. Goto, Y. Hasegawa, and M. Tanaka, ―Efficient Scheduling Focusing on the Duality of MPL Representation,‖ Proc. IEEE Symp. Computational Intelligence in Scheduling (SCIS ’07), pp. 57-64, Apr. 2007, doi:10.1109/SCIS.2007.367670.(Conference proceedings)

[10] J. Williams, ―Narrow-Band Analyzer,‖ PhD dissertation, Dept. of Electrical Eng., Harvard Univ., Cambridge, Mass., 1993. (Thesis or dissertation)

[11] E.E. Reber, R.L. Michell, and C.J. Carter, ―Oxygen Absorption in the Earth’s Atmosphere,‖ Technical Report TR-0200 (420-46)-3, Aerospace Corp., Los Angeles, Calif., Nov. 1988. (Technical report with report number)

[12] L. Hubert and P. Arabie, ―Comparing Partitions,‖ J. Classification, vol. 2, no. 4, pp. 193-218, Apr. 1985. (Journal or magazine citation)

[13] R.J. Vidmar, ―On the Use of Atmospheric Plasmas as Electromagnetic Reflectors,‖ IEEE Trans. Plasma Science, vol. 21, no. 3, pp. 876-880, available at http://www.halcyon.com/pub/journals/21ps03-vidmar, Aug. 1992. (URL for Transaction, journal, or magzine)

[14] J.M.P. Martinez, R.B. Llavori, M.J.A. Cabo, and T.B. Pedersen, "Integrating Data Warehouses with Web Data: A Survey," IEEE Trans. Knowledge and Data Eng., preprint, 21 Dec. 2007, doi:10.1109/TKDE.2007.190746.(PrePrint)

[15] Guang-Hai Liu, Jing-Yu Yang, ―Image retrieval based on the texton cooccurrence matrix,‖ Pattern Recognition, vol.41, pp. 3521-3527, 2008.

[16] Julesz B., ―Textons, The Elements of Texture Perception, and their Interactions,‖ Nature, vol.290 (5802): pp.91-97, 1981.

[17] Julesz B., ―Texton gradients: the texton theory revisited,‖ Biological Cybernetics, vol.54 pp.245–251, 1986.

[18] Narayanan R.M., T.S. Sankaravadivelu, and S.E. Reichenbach, ―Dependence of image information content on gray-scale resolution‖, Proceedings of IGARSS, 24–28 July, Honolulu, Hawaii, 1:153–155, 2000.

[19] VisTex.ColourImageDatabase http:// www.whitemedia.mit.edu/vismod/imagery/VisionTexture.

[20] Google database

[21] B. J. Falkowski and M. A. Perkowski, ―A family of all essential Radix addition/subtraction multipolarity transforms: Algorithms and interpretations in Boolean domain,‖ in Proc. 23rd IEEE Int. Symp. Circuits Systems, New Orleans, LA, May 1990, pp. 1596–1599.

[22] W. K. Pratt, Digital Image Processing. New York: Wiley, 1991.

[23] O. Pichler, A. Teuner, and B. J. Hosticka, ―A comparison of texture feature extraction using adaptive Gabor filtering, pyramidal and tree structured wavelet transforms,‖ Pattern Recognit., vol. 29, pp. 733–742, 1996.

[24] B. Sujatha, Dr.V.Vijayakumar, Dr.U.S.N.Raju, ―Integrated framework for texture classification using statistical and structural approach‖, IJAEST, Vol.12, issue 2, 2011.

Untitled Page