Abstract
Aiming at the problems of traditional image segmentation methods such as single image type and falling into false boundary, a level set image segmentation algorithm based on non-independent and identically distributed (non-IID) is proposed. The traditional image segmentation method basically assumes that all samples are independent and have the same distribution. However, this assumption does not hold. In this paper, the image is segmented into several pixel blocks by super-pixel segmentation method. The feature vectors are extracted from the pixel blocks by non-IID method. We combine the obtained feature vectors with the CV model to change the energy function of the level set method and apply the new energy function to image segmentation. The experimental results show that the proposed algorithm can segment all kinds of images and has a good segmentation effect for weak edge images.
Student as first author.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Osher, S., Sethian, J.A.: Fronts propagating with curvature dependent speed algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)
Vicent, C., Ron, K., Guillermo, S.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)
Tony, F., Luminita, A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Olivier, B., Denis, F., Philippe, T., et al.: Variational b-spline level-set: a linear filtering approach for fast deformable model evolution. IEEE Trans. Image Process. 18(6), 1179–1191 (2009)
Li, C., Huang, R., Ding, Z., et al.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)
Zhang, F., Zhang, X.: Distance regularized level set image segmentation based on dislocation theory. J. Autom. 44(5), 943–952 (2017)
Zhang, Y., Guo, H.: Level set image segmentation based on rough set and new energy formula. J. Autom. 41(11), 1913–1925 (2015)
Zhu, Y., Weng, G.: Evolution model of distance rule level set with energy enhancement of area term. Cartography J. 39(1), 12–20 (2018)
Shi, Y., Li, W., Gao, Y., Cao, L., et al.: Beyond IID: learning to combine non-IID metrics for vision tasks. In: AAAI-17 (2017)
Teney, D., Brown, M., Kit, D., Hall, P.: Learning similarity metrics for dynamic scene segmentation. In: CVPR 2015 (2015)
Cao, L.: Independent and distributed recommendation system: summary and framework of recommendation paradigm conversion. Engineering 2(02), 138–165 (2016)
Torresani, L., Lee, K.: Large margin component analysis. In: NIPS, pp. 1385–1392(2007)
Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. TPAMI 34(11), 2274–2282 (2012)
Zhang, K., Xu, S., Zhou, W., et al.: Active contours based on image laplacian fitting energy. Chin. J. Electron. 18(2), 281–284 (2009)
Ning, J., Zhang, L., Zhang, D., et al.: Interactive image segmentation by maximal similarity-based region merging. Pattern Recogn. 43(2), 445–456 (2010)
Li, C., Kao, C., et al.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)
Tang, M., Gorelick, L., Olga, V., et al.: GrabCut in one cut. In: ICCV 2013 (2013)
Carsten, R., Vladimir, K., Andrew, B.: Grab cut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)
Leo, G.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)
Li, C., Xu, C., et al.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)
Cheng, M., Mitra, N., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37, 569–582 (2015)
Acknowledgments
This work was supported by National Key R&D Program of China (2016YFC0303707).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Lian, Y., Wang, D., Zhang, J. (2019). Level Set Image Segmentation Based on Non-independent and Identically Distributed. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-31723-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31722-5
Online ISBN: 978-3-030-31723-2
eBook Packages: Computer ScienceComputer Science (R0)