International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.8, No.4, Apr. 2016
Edge Information for Boosting Discriminating Power of Texture Retrieval Techniques
Full Text (PDF, 1138KB), PP.16-28
Texture is a powerful image property for object and scene characterization, consequently, a large number of techniques has been developed for describing, classifying and retrieving texture images. On the other hand, edge information is proven to be an important cue used by the human visual system. Several physiological experiments have shown that, when looking at an object, human eyes explore different locations of that object through saccadic eye movements but they spend more time fixating edge regions. Based on this result, we hypothesize that a better performance could be obtained when analyzing an image (texture images in this case) if the visual features extracted from edge regions are given higher weights than those extracted from uniform regions. To check the validity of this hypothesis, we have modified several existing texture retrieval techniques in a way that incorporates the proposed idea and compared their performance with that of the original techniques. The results of the experiments that have been conducted on three common datasets confirmed the effectiveness of the proposed approach, since a significant improvement in the retrieval rate is obtained for all tested techniques. The experiments have also shown an improvement in the robustness to noise.
Cite This Paper
Abdelhamid Abdesselam,"Edge Information for Boosting Discriminating Power of Texture Retrieval Techniques", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.4, pp.16-28, 2016.DOI: 10.5815/ijigsp.2016.04.03
R. M. Shapley and D. J. Tolhurst, Edge Detectors in Human Vision, The Journal of Physiology vol. 229, pp. 165-183, 1973
Marr, D., 1982. Vision. W.H. Freeman and Co., New York.
M. C. Morrone and D. C. Burr, Feature Detection in Human Vision, Proc. of the Royal Society of London, B235, 221-245, 1988
B.W. Tatler, R.J. Baddeley, I.D. Gilchrist, Visual correlates of fixation selection: effects of scale and time, Vision Research, vol. 45, pp. 643–659, March 2005.
R. J. Baddeley, & B.W. Tatler, High frequency edges (but not contrast) predict where we fixate: A Bayesian system identification analysis, Vision Research, vol. 46, pp. 2824-2833, September 2006.
Mihran Tuceryan, Texture Analysis, the Handbook of Pattern Recognition and Computer Vision (2nd Edition), pp 207-248, World Scientific Publishing Co., 1998.
L. Shapiro and G. C. Stockman, Computer Vision, chapter 7, pp 212-225, Prentice Hall, 2001.
B. Zhang, Y. Gao, S. Zhao and J. Liu, Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor, IEEE Trans. On Image Processing, vol. 19, pp. 533-544, February 2010.
U S N Raju, A Sridhar Kumar, B Mahesh, B Eswara Reddy, Texture Classification With High Order Local Pattern Descriptor: Local Derivative Pattern, Global Journal of Computer Science and Technology, Vol. 10 Issue 8, pp September 2010
T. Ojala, M. Pietikäinen, and D. Harwood, A Comparative study of texture measures with classification based on feature distributions. Pattern Recognition. 29, 51-59, 1996.
L. Nanni, A. Lumini, and S. Brahnam, Survey on LBP based texture descriptors for image classification, Expert System With Applications, 39, 3634-3641, 2012.
T. Ojala, M. Pietikäinen and T Mäenpää, Gray scale and rotation invariant texture classification with local binary patterns. In the 6th European Conference on Computer Vision-Part I. Springer-Verlag London, UK, 2000.
T. Ojala, M.Pietikaeinen, and Topi Maeenpaea, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence. 24, 971-987, 2002.
X. Tan and B. Triggs, Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions, Analysis and Modeling of Faces and Gestures (AMFG'07), LNCS, 4778, 168-182, Springer 2007.
Z. Guo, L. Zhang and D. Zhang, A Completed Modeling of Local Binary Pattern Operator for Texture Classification, IEEE Trans. On Image Processing, 19, 1657-1663, 2010.
Z. Guo, L. Zhang and D. Zhang, Rotation Invariant Texture Classification Using LPB Variance (LBPV) with Global Matching, Pattern Recognition, 43, 706-719, 2010.
W. Li, and K. Mao, Designing compact Gabor filter banks for efficient texture feature extraction, 11th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 2010
M. M. H. Daisy, S.T. Selvi, and J. S. G. Mol, Combined texture and shape features for content based image retrieval International Conference on Circuits, Power and Computing Technologies (ICCPCT), Nagercoil, 2013
M.P. Arakeri, and G.R.M. Reddy, A novel CBIR Approach to differential Diagnosis of Liver Tumor on Computed Tomography Images, International Conference on Modeling, Optimization and Computing, Procedia Engineering, 38, 528-536, 2012.
H-J. Wang, H-N. Qi and X-F Wang, A new Gabor based approach for wood recognition, Neurocomputing 116 192–200, 2013
J.R. Smith and S-F. Transform features for texture classification and discrimination in large image databases, Image processing proceedings, ICIP-94, 3, 407-411 1994.
M. Kokare, P.K. Biswas, B.N. Chatterji, Texture image retrieval using rotated wavelet filters, Pattern Recognition Letters, 28, 1240-1249, 2007
P.W. Huang, S.K. Dai, Image retrieval by texture similarity, Pattern Recognition, 36, 665-679, 2003.
P.W. Huang, S.K. Dai, Design of a two-stage content-based image retrieval system using texture similarity, Information Processing and Management 40, 81-96, 2004.
P.W. Huang, S.K. Dai, and P.L.Lin, Texture image retrieval and image segmentation using composite sub-band gradient vectors, J. Vis. Communication and Image Representation 17, 947-957, 2006
K Arai., I. N. Abdullah, H. Okumura, Image Retrieval Based on Color, Shape, and Texture for Ornamental Leaf with Medicinal Functionality Images, IJIGSP, vol.6, no.7, pp.10-18,2014
D-M. Tsai and C-F Tseng, Surface roughness classification for castings, Pattern Recognition 32, 389-405, 1999.
J.S. Weszka, C.R. Dyer, A. Rosenfeld, A comparative study of texture measures for terrain classification, IEEE Trans. System, man and Cybernetics, 6, 269-285, 1976.
A. Abdesselam, Multi-resolution Fourier-based texture image retrieval, Proceedings of the 2009 conference on Information Science, Technology and Applications, pp 72-77, ACM Digital Library
Beck et al, Spatial frequency channels and perceptual grouping in texture segregation, Comp. Vision Graphics and Image Processing, 37, 299-325, 1987
A. Abdesselam, Improving Local Binary Patterns Techniques by Using Edge Information, Lecture Notes on Software Engineering vol. 1, no. 4, pp. 360-363, 2013
http://www.ux.uis.no/~tranden/brodatz.html, accessed in 2009.
http://www.outex.oulu.fi/index.php? accessed in 2012.
http://vismod.media.mit.edu/pub/VisTex , accessed in 2014
S. Hossain and S. Serikawa, Texture databeses – A comprehensive survey, Pattern Recognition Letters, 34, 2007-2022, 2013.