Increased Accuracy in Image Segmentation Considering the Modular Method Based on Texture Characteristics and Super Pixel
Journal of Electrical and Electronic Engineering
Volume 4, Issue 3, June 2016, Pages: 63-67
Received: May 13, 2016;
Accepted: May 23, 2016;
Published: Jun. 4, 2016
Views 2838 Downloads 135
M. Dabbaghha, Department of Telecommunications and Electrical Engineering, Malek Ashtar University of Technology, Tehran, Iran
M. Dashtbayazi, Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
S. Marjani, Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
M. Sabaghi, Laser and Optics Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran
To solve the problem of segmenting an image into homogeneous regions with large area, this paper proposes an efficient algorithm that is based optimization base on modularity and super pixel method. Due to the fact that a very small areas of the image before segmentation, the proposed algorithm automatically merged with neighboring small areas and to make larger modules. When the modularity pictures after the merger to reach its maximum stop, leading to the production of segmentation algorithms are final. To keep repetitive patterns in a homogeneous area, a feature based on its histogram modularity with features like color and eventually identified two areas by creating a similarity matrix, it is suggested. So that the problem of segmentation and complexity of the problem to some extent eliminate in a way that due to the combined areas can be achieved for repetitive patterns. Simulation results show that the algorithm has good accuracy.
Increased Accuracy in Image Segmentation Considering the Modular Method Based on Texture Characteristics and Super Pixel, Journal of Electrical and Electronic Engineering.
Vol. 4, No. 3,
2016, pp. 63-67.
B. Bhanu and J. Peng, “Adaptive integrated image segmentation and object recognition”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 30, no. 4, pp. 427–441, 2000.
H. Zhu, J. Zheng, J. Cai, and N. M. Thalmann, “Object-level image segmentation using low level cues”, IEEE Transactions on Image Processing, 2013.
M. Wertheimer, “Laws of organization in perceptual forms,” A Source Book of Gestalt Psychology, pp. 71–88, 1938.
D. D. Hoffman and M. Singh, “Salience of visual parts,” Cognition, vol. 63, no. 1, pp. 29–78, 1997.
L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations”, IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583–598, 1991.
V. Osma-Ruiz, J. I. Godino-Llorente, N. S´aenz-Lech´on, and P. G´omez- Vilda, “An improved watershed algorithm based on efficient computation of shortest paths”, Pattern Recognition, vol. 40, no. 3, pp. 1078–1090, 2007.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics”, The Proceedings of the 8th IEEE International Conference on Computer Vision, ICCV, vol. 2, pp. 416–423, July 2001.
M. Newman, “Analysis of weighted networks”, Physical Review E, vol. 70, no. 5, p. 056131, 2004.
A. Browet, P.-A. Absil, and P. Van Dooren, “Community detection for hierarchical image segmentation”, Combinatorial Image Analysis. Springer, pp. 358–371, 2011.
W. Li, “Modularity segmentation”, Neural Information Processing. Springer, pp. 100–107, 2013.
G. Mori, “Guiding model search using segmentation,” The Proceedings of the 10th IEEE International Conference on Computer Vision, ICCV, vol. 2, pp. 1417–1423, 2005.
D. Dowson and B. Landau, “The frechet distance between multivariate, normal distribution”, Jornal of multivariate Analysis, vol 12, no 3, pp. 450–455, 1982.
J. Puzicha, J. Buhmann, Y. Rubner, and C. Tomasi, “Empirical evaluation of dissimilarity measures for color and texture”, The Proceedings of the 7th IEEE International Conference on Computer Vision, ICCV, Kerkyra, Greece, pp. 1165–1172, 1992.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection” IEEE Conference on Computer Vision and Pattern Recognition, CVPR, vol. 1, pp. 886–893, 2005.
D. R. Martin, c. c. Fowlkes, and J. Malik, “Learning to detect natural image boundaries, color, andtexture cues”, IEEE Transaction on pattern Analysis and machine intelligence, vol, 26, no. 5, pp. 530–549, 2004.
A. Yang, J. Wright, Y. Ma, and S. Sastry, “Unsupervised segmentation of natural image via lossy data compression”, Computer version and image understanding, vol 110, no 2, pp. 212–225, 2008.
C. Couprie, L. Grady. L. Najman, and H. Talbot, “Power watershed: A unifying graph-based optimization framework,” IEEE Transaction on Pattern analysis and Machine Intelligence, vol. 33, no. 7, pp. 1384–1399. 2011.