Open Access   Article Go Back

Comparison of Texture Extraction & Segmentation of Complex Images using PCA_GMTD & RP-Live Wire Algorithms

Pradnya A. Maturkar1 , M.A. Gaikwad2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 952-961, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.952961

Online published on Sep 30, 2018

Copyright © Pradnya A. Maturkar, M.A. Gaikwad . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Pradnya A. Maturkar, M.A. Gaikwad, “Comparison of Texture Extraction & Segmentation of Complex Images using PCA_GMTD & RP-Live Wire Algorithms,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.952-961, 2018.

MLA Style Citation: Pradnya A. Maturkar, M.A. Gaikwad "Comparison of Texture Extraction & Segmentation of Complex Images using PCA_GMTD & RP-Live Wire Algorithms." International Journal of Computer Sciences and Engineering 6.9 (2018): 952-961.

APA Style Citation: Pradnya A. Maturkar, M.A. Gaikwad, (2018). Comparison of Texture Extraction & Segmentation of Complex Images using PCA_GMTD & RP-Live Wire Algorithms. International Journal of Computer Sciences and Engineering, 6(9), 952-961.

BibTex Style Citation:
@article{Maturkar_2018,
author = {Pradnya A. Maturkar, M.A. Gaikwad},
title = {Comparison of Texture Extraction & Segmentation of Complex Images using PCA_GMTD & RP-Live Wire Algorithms},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {952-961},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2970},
doi = {https://doi.org/10.26438/ijcse/v6i9.952961}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.952961}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2970
TI - Comparison of Texture Extraction & Segmentation of Complex Images using PCA_GMTD & RP-Live Wire Algorithms
T2 - International Journal of Computer Sciences and Engineering
AU - Pradnya A. Maturkar, M.A. Gaikwad
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 952-961
IS - 9
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
625 250 downloads 304 downloads
  
  
           

Abstract

In our proposed research work, the application of live-wire algorithm has been proposed to segment complex Aerial insulator images along with RP algorithm to extract the features of images. Firstly, Gray Level Co-occurrence Matrix (GLCM) is employed to extract the texture features of image by the rapid Gray Level Co-occurrence Integrated Algorithm (GLCIA). We have categorised extracted texture feature into two: one with the stronger discriminative ability and other with weaker ability. The weaker discriminative ability has optimized by using PCA & RP. Successfully segmenting the complex low contrast aerial images is one of the main focus of this paper.After optimization by PCA, segmentation is done by Global Minimization with Texture Descriptor (GMTD).After optimization by RP, segmentation is done by Live-wire. To analyze the comparative effect of algorithms on optimization & segmentation, we have used Brodatz dataset. We have observed that Random projection is computationally faster than PCA due to random selection of ‘best’ basis vector which improves the computational speed of RP. Live wire is use to fast segmentation as well as improves weight parameter. Our results demonstrate a 20% improvement in overall system speed and 10% improvement in segmentation accuracy when compared with traditional algorithms .Another advantage of using this technique is that the process is fully automatic thus can be used for training of machine learning and AI based algorithms.

Key-Words / Index Term

GLCM,GLCIA,Segmentation,GMTD

References

[1] Gonzalez Book “Digital Image Processing”,(3rd edition)
[2] Qing gang Wu, Jubai An, and Bin Lin “A Texture Segmentation Based On PCA & Global Minimization Active Contour Model For Aerial Insulator Images” IEEE journal of selected topics in applied earth observations and remote sensing accepted April 11,2012.
[3] Li Liu and Paul W. Fieguth, Member, IEEE “Texture Classification from Random Features”, IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 3, march 2012
[4] D. A. Clausi, “Comparison and fusion of co-occurrence, Gabor and MRF texture features for classification of SAR sea-ice imagery,”Atmos.-Ocean, vol.39, no.3, pp. 183–194, 2000.
[5] automatic recognition of human settlements in arid regions with scattered vegetation,” IEEE J. Sel. Topics Appl. Earth Observ.Remote Sens. (JSTARS), vol. 4, no. 1, pp. 16–26, 2011
[6] P. R. Chowdhury, B. Deshmukh, A. K. Goswami, and S. S. Prasad, “Neural network based dunal landform mapping from multispectral images using texture features,” IEEE J. Sel. Topics Appl. Earth Observ.Remote Sens. (JSTARS), vol. 4, no. 1, pp. 171–184, 2011.
[7] A. A. Ursani, K. Kpalma, C. C. D. Lelong, and J. Ronsin, “Fusion of textural and spectral information for tree crop and other agricultural cover mapping with very-high resolution satellite images, ”IEEEJ.Sel.Topics Appl. Earth Observ.Remote Sens. (JSTARS) ,vol. , no1 , pp.225–235, 2012.
[8] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification, ” IEEE Trans. Syst., Man, Cybern., vol. 3, no. 6, pp. 610–621, 1973.R. F. Walker, P. T. Jack way, and D. Long staff, “Genetic algorithm optimization of adaptive multiscale GLCM features,” Int. J. Pattern Recogn. Artificial Intell., vol. 17, no. 1, pp. 17–39, 2003.
[9] D.A. Clausi and Y. Zhao, “Grey Level Co-occurrence Integrated Algorithm (GLCIA): A superior computational method to rapidly determine co-occurrence probability texture features,” Comput. Geosci., vol. 29,no. 7, pp. 837–850, 2003.
[10] R.F.Walker, P. T. Jackway, and D. Longstaff, “Genetic algorithm optimization of adaptive multiscale GLCM features,” Int. J. Pattern Recogn. Artificial Intell, vol. 17, no. 1, pp. 17–39, 2003.
[11] W.B. Johnson and J. Lindenstrauss, “Extensions of Lipschitz Mappings into a Hilbert Space,” Proc. Conf. Modern Analysis and Probability, pp. 189-206, 1984.
[12] S.Dasgupta and A. Gupta, “An Elementary Proof of a Theorem of Johnson and Lindenstrauss,” Random Structures and Algorithms, vol. 22, no. 1, pp. 60-65, 2003.
[13] E.Bingham and H. Mannila, “Random Projection in Dimensionality Reduction: Applications to Image and Text Data,” Proc. Seventh ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 245-250, 2001.
[14] S.Dasgupta, “Experiments with Random Projections,” Proc. 16th Conf. Uncertainty in Artificial Intelligence, pp. 143-151, 2000.
[15] X.Z. Fern and C.E. Brodley, “Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach,” Proc. 20th Int’l Conf. Machine Learning, 2003.
[16] E.J. Cande`s and T. Tao, “Decoding by Linear Programming” IEEE Trans. Information Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.
[17] E.J. Cande`s and T. Tao, “Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies?” IEEE Trans. Information Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
[18] D.L.Donoho, “Compressed Sensing,” IEEE Trans. Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
[19] G. Biau, L. Devroye, and G. Lugosi, “On the Performance of Clustering in Hilbert Spaces,” IEEE Trans. Information Theory,vol. 54, no. 2, pp. 781-790, Feb. 2008.
[20] R.G. Baraniuk, M. Davenport, R.A. DeVore, and M. Wakin, “A Simple Proof of the Restricted Isometry Property for Random Matrices,” Constructive Approximation, vol. 28, no. 3, pp. 253-263,2008.
[21] D.Achlioptas, “Database-Friendly Random Projections,” Proc. 20th ACM Symp. Principles of Database Systems, pp. 274-281, 2001.
[22] N. Goel, G. Bebis, and A. Nefian, “Face Recognition Experiments with Random Projection,” Proc. SPIE, vol. 5779, pp. 426-437, 2005.
[23] Li, ping, Trevor J Hastie and Kenneth W. Church “Very sparse random projection” Proceedings of 12th ACM SIKDD international conf.on knowledge discovery and data mining ACM 2006.
[24] P.A.Maturkar, Dr.M.A.Gaikwad “RP-live wire algorithms” Proceeding of by IEEE explorer digital library International Conference On “Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017),pp. 74-78 dt. 21st & 22nd Sept. 2017
[25] P.A.Maturkar, Dr.M.A.Gaikwad “Texture Feature Optimization of complex Images using RP&PCA and segmentation by GMTD” Proceeding by IEEE International conference on “Resent innovation in Electrical, Electronics & communicationEngineering-ICRIEECE-2018”, Bhubaneswar, Dt.27th & 28th July 2018
[26] Xiao-Feng Wang, Hai Min, Yi-Gang Zhang, “Multi-scale local region based level set method for image segmentation in the presence of intensity in homogeneity”, Elsevier March 2015 Wanceng Zhang, Xian Sun, Hongqi Wang, Kun Fu, “A generic discriminative part-based model for geospatial object detection in optical remote sensing images”, Elsevier January 2015.
[27] Wanceng Zhang, Xian Sun, Hongqi Wang, Kun Fu, “A generic discriminative part-based model for geospatial object detection in optical remote sensing images”, Elsevier January 2015.
[28] Jian Yang, Peijun Li, Yuhong He, “A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation”, Elsevier August 2014.
[29] Jing Liu, Peijun Li , Xue Wang, “A new segmentation method for very high resolution imagery using spectral and morphological information”, Elsevier March 2015.
[30] Zhijian Huang, Jinfang Zhang, Fanjiang Xu, “A novel multi-scale relative salience feature for remote sensing image analysis”, Elsevier January 2014
[31] Chao Wang, Ai-Ye Shi, Xin Wang and et al., “A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm”, Elsevier October 2014.
[32] Xiang-Yang Wang, Xian-Jin Zhang and et al., “A pixel-based color image segmentation using support vector machine and fuzzy C-means”, Elsevier September 2012.
[33] Jorge E. Patino, Juan C. Duque ,“A review of regional science applications of satellite remote sensing in urban settings”, Elsevier January 2013.
[34] Zhongwu Wang, John R. Jensen, Jungho Im, “An automatic region-based image segmentation algorithm for remote sensing applications”, Elsevier October 2010.
[35] Jianqiang Gao, Lizhong Xu ,“An efficient method to solve the classification problem for remote sensing image”, Elsevier January 2015.
[36] Saman Ghaffarian, Salar Ghaffarian, “Automatic histogram-based fuzzy C-means clustering for remote sensing imagery”, Elsevier November 2014.
[37] Xueliang Zhang, Pengfeng Xiao, Xiaoqun Song, Jiangfeng She, “Boundary-constrained multi-scale segmentation method for remote sensing images”, Elsevier April 2013.
[38] Stelios K. Mylonas, Dimitris G. Stavrakoudis, John B. Theocharis ,“GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images”, Elsevier December 2013.
[39] Xueliang Zhang, Pengfeng Xiao and et al., “Hybrid region merging method for segmentation of high-resolution remote sensing images”, 2014.
[40] Maire, M.; Fowlkes, C.; Malik, J.; “Contour Detection and Hierarchical Image Segmentation”, IEEE 2011.
[41] Jianyu Chen, Delu Pan, Qiankun Zhu and et al., “Edge-Guided Multi scale Segmentation of Satellite Multispectral Imagery”, IEEE 2012.
[42] P. Brodatz, Texture – A Photographic Album for Artists and Designers. New York: Reinhold, 1968.
[43] A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran “Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques”Vol.6, Issue.9, pp.315-323, Sep-2018
[44] S. Pathak, V. Sejwar “A Review on Image Segmentation Using Different Optimization Techniques”Vol.5 , Issue.5 , pp.217-221, May-2017