Object Segmentation using Local Histograms, Invasive Weed Optimization Algorithm and Texture Analysis | ||
| Journal of AI and Data Mining | ||
| دوره 9، شماره 4، بهمن 2021، صفحه 439-449 اصل مقاله (2.45 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2021.10200.2158 | ||
| نویسندگان | ||
| S. Bayatpour؛ Seyed M. H. Hasheminejad* | ||
| Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran. | ||
| چکیده | ||
| Most of the methods proposed for segmenting image objects are supervised methods which are costly due to their need for large amounts of labeled data. However, in this article, we have presented a method for segmenting objects based on a meta-heuristic optimization which does not need any training data. This procedure consists of two main stages of edge detection and texture analysis. In the edge detection stage, we have utilized invasive weed optimization (IWO) and local thresholding. Edge detection methods that are based on local histograms are efficient methods, but it is very difficult to determine the desired parameters manually. In addition, these parameters must be selected specifically for each image. In this paper, a method is presented for automatic determination of these parameters using an evolutionary algorithm. Evaluation of this method demonstrates its high performance on natural images. | ||
| کلیدواژهها | ||
| Object segmentation؛ Local threshold؛ Histogram؛ invasive weed optimization؛ Texture analysis | ||
| مراجع | ||
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[1] A.R.Mehrabian, and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization” Ecological Informatics1, pp. 355-366, 2006.
[2] N. Otsu, “A threshold selection method from gray-level histogram” IEEE Transactions on Man & Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.
[3] G. Ribons, “Color edge detection”, Optical Engineering, Vol. 16, No. 5, 1966.
[4] J.M.S. Prewitt and M.L. Mendelsohn, “The analysis of cell images” Annals of the New York Academy Science, Vol. 128, pp. 1035-1053, 1966.
[5] M.S. Al-tarawneh, “Lung cancer detection using image processing techniques” Leonardo Electronic Journal of Practices and Technologies, Issue 20, pp. 147-158, 2012.
[6] R. Rodriguez, “A Robust Algorithm for Binarization of Objects” Latin American Applied Research, Vol. 40, 2010.
[7] H. Jie, Z. Xiaojun, Y. Chunhua, and G. Weihua, “A multi-threshold image segmentation approach using state transition algorithm,” in Proceedings of the 34th Chinese Control Conference, July, pp. 28-30, 2015.
[8] R.C. Gonzalez and R.E. Woods, “Intensity transformation and spatial filtering” in Digital Image Processing, 3rd ed. Prentice Hall, 2008.
[9] S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama, “Robust Object Segmentation using Genetic Optimization of Morphological Processing Chains,” in Proceedings of the 5th WSEAS International Conference on signal, speech and image processing, pp. 248-253, August 2005.
[10] K.S.N. Ripon, L.E. Ali, S. Newaz, and J. Ma, “A Multi-objective Evolutionary Algorithm for Color Image Segmentation” Mining Intelligence and Knowledge Exploration. MIKE. Lecture Notes in Computer Science, Vol. 10682, 2017.
[11] U. Kirchmaier, S. Hawe and K. Diepold, “A Swarm Intelligence Inspired Algorithm for Contour Detection in Images” Applied Soft Computing, Vol. 13, Issue 6, pp. 3118-3129, 2013.
[12] W. Fu, M. Zhang and M. Johnston, “Bayesian Genetic Programming for Edge Detection” Soft Computing, Vol. 23, Issue 12, pp. 4097-4112, 2019.
[13] W. Tao, H. Jin, and L. Liu, “Object segmentation using ant colony optimization algorithm and fuzzy entropy” Pattern Recognition Letters, Vol. 28, pp. 788–796, 2007.
[14] L. Xu, H. Jia, C. Lang, X. Peng, and K. Sun, “A Novel Method for Multi-level Color Image Segmentation based on Dragonfly Algorithm and Differential Evolution”, IEEE Access, Vol. 7, pp. 19502-19538, 2019.
[15] X. Zhao, M. Turkb, W. Lid, K. Lien, and G. Wang, “A multi-level image thresholding segmentation algorithm based on two-dimensional K–L divergence and modified particle swarm optimization” Applied Soft Computing, Vol. 48, pp. 151-159, 2016.
[16] O. Banimelhem, and Y.A. Yahya, “Multi-thresholding Image Segmentation using Genetic Algorithm”, In World Congress in Computer Science, Computer Engineering, and Applied Computing, 2011.
[17] Y. Zhang and L. Wu, “Optimal Multi-level Thresholding based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach” Entropy, Vol. 13, pp. 841-859, 2011.
[18] C.C. Lai, “A Novel Image Segmentation Approach based on Particle Swarm Optimization” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E89-A, No.1, pp. 324-327, 2006.
[19] P. Moallem, and N. Razmjooy, “Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization” Journal of Applied Research and Technology, Vol. 10, No. 5, 2012.
[20] P.Y. Yin and L.H. Chen, “A fast iterative scheme for multi-level thresholding methods” Signal Processing, Vol. 60, pp. 305-313, 1997.
[21] S.A. Mohammadi, R. Akbari and S.H. Mohammadi, “An Efficient Method based on ABC for Optimal Multi-level Thresholding” IJST Trans. Of Electrical Engineering, Vol. 36, No. E1, pp. 37-49, 2012.
[22] J. Musavirad, and H. Ebrahimpour, “Optimal Multilevel Image Thresholding using an Optimization Algorithm based on Learning and Teaching” Journal of Image Processing and Machine Vision, 2nd Year, No. 2, 2015, Farsi.
[23] P. Kanungo, P.K. Nanda and , U.C. Samal “Image segmentation using thresholding and genetic algorithm,” in Proceedings of the Conference on Soft Computing Technique for Engineering Applications, SCT, Rourkela, India, pp. 24-26, March 2006.
[24] J. Yang, Y. Yang, W. Yu and J. Feng, “Multi-threshold image segmentation based on K-means and Firefly Algorithm,” in 3rd International Conference on Multimedia Technology, ICMT, 2013.
[25] P. Arbelaez, M. Maire, C. Fowlkes and J. Malik, “Contour detection and hierarchical image segmentation” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33 (5), pp. 898–916, 2011.
[26] D. Comaniciu, and P. Meer, “Mean shift: a robust approach toward feature space analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24 (5), pp. 603 –619, 2002.
[27] T. Cour, F. Benezit, and J. Shi, “Spectral segmentation with multi-scale graph decomposition,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1124–1131, 2005.
[28] P.F. Felzenszwalb and D.P. Huttenlocher, “Efficient graph-based image segmentation” International Journal of Computer Vision, Vol. 59 (2), pp. 167–181, 2004.
[29] M. Maire, P. Arbelaez, C. Fowlkes and J. Malik, “Using contours to detect and localize junctions in natural images” CVPR computer Vision and Pattern Recognition, 2008.
[30] M.H. Hasheminezhad and S. Bayatpour, “Image Segmentation using Local Thresholds and Genetic Algorithm,” in 2nd International conference on computer engineering and information technology, Tehran, Iran, 2017 [Farsi].
https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html. doi = {10.1109/TPAMI.2010.161}
[32] R. Aslanzadeh, K. Qazanfari and M. Rahmati, “An Efficient Evolutionary based Method for Image Segmentation,” arXiv:1709.04393, 2017 [online].
[33] M.H. Arsay, Suyanto, and K.N. Ramadhani, “Aerial Image segmentation with clustering using firework algorithm” Indonesian journal of computing, 2019.
[34] K.S. Ripon, L.E. Ali, S. Newaz, and J. Ma, “A Multi-objective Evolutionary Algorithm for Color Image Segmentation” Lecture notes in computer science, 2017.
[35] N. Widynski and M. Mignotte “A Multi-scale Particle Filter Framework for Contour Detection” pattern analysis and machine intelligence, Vol. 36, No. 10, 2014.
[36] Z. Dorrani and M.s. Mahmoodi “Noisy images edge detection: Ant Colony Optimization algorithm” International Journal of AI and Data mining, Vol.4, pp. 77-83, 2016. | ||
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