Abstract
Object segmentation is used in multiple image processing applications. It is generally difficult to perform the object segmentation fully automatically. Most object segmentation schemes are developed based on prior information, training process, existing annotation, special mechanical settings or the human visual system modeling. We proposed a fully automatic segmentation method not relying on any training/learning process, existing annotation, special settings or the human visual system. The automatic object segmentation is accomplished by an objective object weight detection and modified GrabCut segmentation. The segmentation approach we propose is developed only based on the inherent image features. It is independent with various datasets and could be applied to different scenarios. The segmentation result is illustrated by testing a large dataset.
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
References
Kohli, P., Torr, P.H.: Dynamic graph cuts for efficient inference in markov random fields. IEEE Trans. pattern Anal. Mach. Intel. 29(12), 2079–2088 (2007)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. In: ACM Transaction Graphics (TOG), vol. 23, ACM, pp. 309–314 (2004)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)
Boykov, Y.Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: 2001 Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 105–112. IEEE (2001)
Chuang, Y.-Y., Curless, B., Salesin, D.H., Szeliski, R.: A Bayesian approach to digital matting. In: 2001 Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 2, p. II. IEEE (2001)
Mortensen, E.N., Barrett, W.A.: Intelligent scissors for image composition. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pp. 191–198. ACM (1995)
Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3241–3248. IEEE (2010)
Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE Trans. Pattern Anal. Mach. Intel. 34(7), 1312–1328 (2012)
Won, C.S., Pyun, K., Gray, R.M.: Automatic object segmentation in images with low depth of field. In: 2002 Proceedings 2002 International Conference on Image Processing 2002, vol. 3, pp. 805–808. IEEE (2002)
Campbell, N.D., Vogiatzis, G., Hernández, C., Cipolla, R.: Automatic 3D object segmentation in multiple views using volumetric graph-cuts. Image Vis. Comput. 28(1), 14–25 (2010)
Jung, C., Kim, C.: A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. IEEE Trans. Image Process. 21(3), 1272–1283 (2012)
Fu, Y., Cheng, J., Li, Z., Lu, H.: Saliency cuts: an automatic approach to object segmentation. In: 2008 19th International Conference on Pattern Recognition ICPR 2008, pp. 1–4. IEEE (2008)
Harel, J., Koch, C., Perona, P., et al.: Graph-based visual saliency. In: NIPS, vol. 1, p. 5 (2006)
Schauerte, B., Stiefelhagen, R.: Quaternion-based spectral saliency detection for eye fixation prediction. In: Computer Vision–ECCV 2012, pp. 116–129. Springer (2012)
Schauerte, B., Stiefelhagen, R.: Predicting human gaze using quaternion DCT image signature saliency and face detection. In: 2012 IEEE Workshop on Applications of Computer Vision (WACV), pp. 137–144. IEEE (2012)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545–552 (2007)
Jiang, F., King, B.: A novel quality assessment for visual secret sharing schemes. EURASIP J. Inf. Secur. 2017(1), 1 (2017)
Blumenthal, I.: Grab cut (2012). www.grabcut.weebly.com/
Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive image segmentation using an adaptive GMMRF model. In: Computer Vision-ECCV, 2004, pp. 428–441 (2004)
Kim, J., Parra, A., Li, H., Delp, E.J.: Efficient graph-cut tattoo segmentation. In: SPIE/IS&T Electronic Imaging, International Society for Optics and Photonics, pp. 94100H–94100H (2015)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Computer vision and Image understanding 106(1), 59–70 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, F., Pang, Y., Lee, T.N., Liu, C. (2020). Automatic Object Segmentation Based on GrabCut. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-17795-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17794-2
Online ISBN: 978-3-030-17795-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)