Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine
Subject Areas : IT StrategyMasoumeh Rezaei 1 , mansoureh rezaei 2 , Masoud Rezaei 3
1 - Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2 - Computer Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran
3 - Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Keywords: Foreground-Background Segmentation, Support vector machine, k-means clustering, saliency map,
Abstract :
Foreground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e.g., in video surveillance. In this paper, we introduce an automatic and efficient Foreground-background segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, K-means clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.
[1] X. Y. Wang, W. W. Sun, Z. F. Wu, H. Y. Yang, "Color image segmentation using PDTDFB domain hidden Markov tree model", Applied Soft Computing, Vol. 29, 2015, pp. 138-152.
[2] A. Dirami, K. Hammouche, M. Diaf, P. Siarry, P., "Fast multilevel thresholding for image segmentation through a multiphase level set method", Signal processing, 93(1), 2013, pp. 139-153.
[3] H. Cai, Z. Yang, X. Cao, W. Xia, X. Xu, "A new iterative triclass thresholding technique in image segmentation", IEEE transactions on image processing, Vol. 23, No. 3, 2014, pp.1038-1046.
[4] L. U. Ambata and E. P. Dadios, "Foreground Background Separation and Tracking", International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment (HNICEM), 2019, pp. 1-6.
[5] F. A. Khan, M. Nawaz, M. Imran, A. U. Rahman and F. Qayum, "Foreground detection using motion histogram threshold algorithm in high-resolution large datasets", Multimedia Systems, 2020, pp. 1-12.
[6] M. Castillo-Martinez, F. J. GallegosFunes, B. E. Carvajal- Gamez, G. Urriolagoitia-Sosa, A. J. Rosales-Silva, "Color index based thresholding method for background and foreground segmentation of plant images", Computers and Electronics in Agriculture, Vol. 178, 2020, p. 105783.
[7] J. Canny, "A computational approach to edge detection", IEEE Transactions on pattern analysis and machine intelligence, Vol. 93, No. 6, 1986, pp. 679-698.
[8] J. M. Prewitt, "Object enhancement and extraction", Picture processing and Psychopictorics, Vol. 10, No. 1, 1970, pp. 15-19.
[9] R. C. Gonzalez and R. E. Woods, "Digital image processing", 2002.
[10] T. Uemura and G. Koutaki and K. Uchimura, "T. Uemura and G. Koutaki and K. Uchimura", International Journal of Innovative computing, Information and control, Vol. 7, No. 10, 2011, pp. 6073-6083.
[11] D. Díaz-Pernil, A. Berciano, F. Peña-Cantillana and M. A. Gutiérrez-Naranjo, "Segmenting images with gradient-based edge detection using membrane computing", Pattern Recognition Letters, Vol. 34, No. 8, 2013, pp. 846-855.
[12] C. Panagiotakis, I. Grinias and G. Tziritas, "Natural image segmentation based on tree equipartition, bayesian flooding and region merging", IEEE Transactions on Image Processing, Vol. 20, No. 8, 2011, pp. 2276-2287.
[13] J. Ning, D. Zhang and C. Wu and F. Yue, "Automatic tongue image segmentation based on gradient vector flow and region merging", Neural Computing and Applications, Vol. 21, No. 8, 2012, pp. 1819-1826.
[14] L. Wang, H. Wu and C. Pan, "Region-based image segmentation with local signed difference energy", Pattern Recognition Letters, Vol. 34, No. 6, 2013, pp. 637-645.
[15] S. E. Ebadi and E. Izquierdo, "Foreground segmentation with tree-structured sparse RPCA", IEEE transactions on pattern analysis and machine intelligence, Vol. 40, No. 9, 2017, pp. 2273-2280.
[16] Y. Boykov, O. Veksler and R. Zabih, "Fast approximate energy minimization via graph cuts", IEEE Transactions on pattern analysis and machine intelligence, Vol. 23, No. 11, 2001, pp. 1222-1239. [17] T.M. Nguyen and Q. J. Wu, "Fast and robust spatially constrained Gaussian mixture model for image segmentation", IEEE transactions on circuits and systems for video technology, Vol. 23, No. 4, 2012, pp. 621-635.
[18] O. O. Karadag and F. T. Y. Vural, "Image segmentation by fusion of low level and domain specific information via Markov Random Fields", Pattern Recognition Letters, Vol. 46, 2014, pp. 75-82.
[19] N. Dhanachandra, K. Manglem and Y. J. Chanu, "Image segmentation using K-means clustering algorithm and subtractive clustering algorithm", Procedia Computer Science, Vol. 54, 2015, pp. 764-771.
[20] Y. Yang, Y. Wang and X. Xue, "A novel spectral clustering method with superpixels for image segmentation", Optik, Vol. 127, No. 1, 2016, pp. 161-167.
[21] L. A. Lim and H. Y. Keles, "Foreground segmentation using convolutional neural networks for multiscale feature encoding", Pattern Recognition Letters, Vol. 112, 2018, pp. 256-262.
[22] A. Shahbaz and K. H. Jo, "Deep Foreground Segmentation using Convolutional Neural Network", IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019, p. 103334.
[23] P. Patil and S. Murala, "Fggan: A cascaded unpaired learning for background estimation and foreground segmentation", IEEE Winter Conference on Applications of Computer Vision (WACV), 2019, pp. 1770-1778.
[24] D. Sakkos and E. S. Ho and H. P. Shum, "Illumination-aware multi-task GANs for foreground segmentation", IEEE Access, Vol. 7, 2019, pp. 10976-10986.
[25] J. Liang and Y. Xue and J. Wang, "Genetic programming based feature construction methods for foreground object segmentation", Engineering Applications of Artificial Intelligence", Vol. 89, 2020, p. 103334.
[26] Z. Yu, H. S. Wong and G. Wen, "A modified support vector machine and its application to image segmentation", Image and Vision Computing, Vol. 29, No. 1, 2011, pp. 29-40.
[27] X. Y. Wang, Q. Y. Wang, H. Y. Yang and J. Bu, "Color image segmentation using automatic pixel classification with support vector machine", Neurocomputing, Vol. 74, No. 18, 2011, pp. 3898-3911.
[28] X. Bai and W. Wang, "Saliency-SVM: An automatic approach for image segmentation", Neurocomputing, Vol. 136, 2014, pp. 243-255.
[29] M. K. Sangale and N. B. Kadu, "Real-time Foreground Segmentation and Boundary Matting for Live Videos using SVM".
[30] C. Tang and M. O. Ahmad and C. Wang, "Foreground segmentation in video sequences with a dynamic background", 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018, pp. 1-6.
[31] L. U. Shuhan and S. J. YE, "Using an image segmentation and support vector machine method for identifying two locust species and instars", Journal of Integrative Agriculture, Vol. 19, No. 5, 2020, pp. 1301-1313.
[32] N. Dhanachandra, K. Manglem, and Y. Chanu, "Image segmentation using K-means clustering algorithm and subtractive clustering algorithm". Procedia Computer Science, 54, 2015, pp. 764-771.
[33] W. Chen, C. He, C. Ji, M. Zhang, S. and Chen, "An improved K-means algorithm for underwater image background segmentation", Multimedia Tools and Applications, 80(14), 2021, pp. 21059-21083.
[34] Y. Yang, H. Bilen, Q. Zou, W. Y. Cheung, X. Ji, "Learning Foreground-Background Segmentation from Improved Layered GANs", In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2524-2533), 2022.
[35] B. E. Boser, I. M. Guyon and V. N. Vapnik, "A training algorithm for optimal margin classifiers", In Proceedings of the fifth annual workshop on Computational learning theory, 1992, pp. 144-152.
[36] V. N. Vapnik, "Statistical learning theory", Wiley, New York, 1998.
[37] M. A. Aizerman,"Theoretical foundations of the potential function method in pattern recognition learning",Automation and remote control, Vol. 25, 1964, pp.821-837.
[38] S. Lloyd,"Least squares quantization in PCM", IEEE transactions on information theory, Vol. 28, No. 2, 1982, pp. 129-137.
[39] C. Guo and L. Zhang, "A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression", IEEE transactions on image processing, Vol. 19, No. 1, 2009, pp. 185-198.
[40] P. Soille, "Morphological image analysis: principles and applications", Springer Science and Business Media, 2013.
[41] R. Achanta and Sh. Hemami and F. Estrada and S. Susstrunk, "Frequency-tuned salient region detection", IEEE conference on computer vision and pattern recognition, 2009, pp. 1597-1604.
[42] R. Unnikrishnan, C. M. Pantofaru and M. Hebert, "Toward objective evaluation of image segmentation algorithms", IEEE transactions on pattern analysis and machine intelligence, Vol. 29, No. 6, 2007, pp.929-944.
[43] M. Meila,"Comparing clusterings—an information based distance",Journal of multivariate analysis, Vol. 98, No. 5, 2007, pp. 873-895.
[44] D. Martin and C. Fowlkes and D. Tal and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics", Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, Vol. 2, 2001, pp. 416-423.
