Interactive Natural Image Segmentation and Foreground Extraction


  • Y David Solomon Raju
  • D Krishna Reddy





Segmentation, foreground extraction, clustering, green’s polynomial function.


Interactive image segmentation is very practical and important problem in computer vision.  In this paper a regressive based Green’s function is employed to formulate the problem of segmentation. The method is incorporated with different clustering approaches intended to extract the foreground regions from the natural images. The method performance is improved with proper labeling of foreground and background regions, and with more number of cluster regions. The method is evaluated with two standard benchmark datasets and found that the experimental results were promising.




[1] Xiang S, Nie F & Zhang C, “Semi-Supervised Classification via Local Spline Regressionâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.11, (2010), pp.2039-2053.

[2] Xin J, Renjie Z & Shengdong N, “Image Segmentation Based on Level Set Methodâ€, Physics Procedia, Vol.33, (2012).

[3] Kass M, Witkin A & Terzopoulos D, “Snakes: Active contour modelsâ€, International Journal of Computer Vision, Vol.2, (1988), pp.321-331.

[4] Cousty J, Bertrand G, Najman L & Couprie M, “Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principleâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.31, No.8, (2009), pp.1362-1374.

[5] Grady, L, “Random Walks for Image Segmentationâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.28, No.11, (2006), pp.1768-1783.

[6] Boykov Y & Funka Lea G, “Graph cuts and efficient image segmentationâ€, International Journal of Computer Vision, Vol.69, No.2, (2006), pp.109-131.

[7] Zhou D, Bousquet O, Lal T, Weston J & Scholkopf B, “Learning with local and global consistencyâ€, Adv. Neural Inf. Process. Syst., (2003), pp.321-328.

[8] Wang F & Zhang C, “Label propagation through linear neighborhoodsâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.20, No.1, (2008), pp.55–67.

[9] Ando RK & Zhang T, “Two-view feature generation model for semi supervised learningâ€, International Conference on Machine Learning, (2007), pp. 25–32.

[10] A Tutorial on Clustering Algorithms Introduction, K-means, Fuzzy C-means, Hierarchical, Mixture of Gaussians

[11] Selvathi D & Dhivya R, “Segmentation of tissues in MR images using Modified Spatial Fuzzy C Means algorithmâ€, International Conference on Signal Processing, Image Processing & Pattern Recognition, (2013), pp. 136-140.

[12] Pourkamali-Anaraki, F & Becker, S. Preconditioned data sparsification for big data with applications to PCA and K-means. IEEE Transactions on Information Theory, Vol.63, No.5, (2017), pp.2954-2974.

[13] Cai W, Chen S & Zhang D, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentationâ€, Pattern Recognit., Vol.40, No.3, (2007), pp.825–838.



[16] Vezhnevets V & Konouchine V, “GrowCut: Interactive multi-label ND image segmentation by cellular automataâ€, Proc. of Graphicon., Vol.1, (2005), pp.150–156.

[17] David SRY & Krishna RD, “Interactive Natural Image Segmentation With Regression Based Clustering Algorithmâ€, International Journal of Latest Trends in Engineering and Technology Vol.8, No. 1, (2015), pp.494-497

[18] Yi X & Eramian M, “LBP-Based Segmentation of Defocus Blurâ€, IEEE Transactions on Image Processing, Vol.25, No.4, (2016), Pp.1626-1638.

[19] Villalobos Antúnez, JV (2017). Karl R. Popper, Heráclito y la invención del logos. Un contexto para la Filosofía de las Ciencias Sociales. Opción Vol. 33, Núm. 84. 5-11

[20] M Pallarès Piquer and O Chiva Bartoll (2017). La teoría de la educación desde la filosofía de Xavier Zubiri. Opción, Año 33, No. 82 (2017): 91-113

View Full Article: