Abstract
Using human prior information to perform interactive segmentation plays a significant role in figure/ground segmentation. In this paper, we propose an active learning based approach to smartly guide the user to interact on crucial regions and can quickly achieve accurate segmentation results. To select the crucial regions from unlabeled candidates, we propose a new criterion, i.e. selecting the ones which maximize the expected confidence change (ECC) over all unlabeled regions. Given an image represented by oversegmented regions, our active learning based approach iterates following three steps: 1) selecting crucial unlabeled regions with maximal ECC; 2) refining the selected regions; 3) updating appearance models based on the refined regions and performing image segmentation. Specifically, a constrained random walks algorithm is employed for segmentation, since it can efficiently produce confidence for computing ECC during active learning. Compared to the conventional interactive segmentation methods, the experimental results demonstrate our method can largely reduce the interaction efforts while maintaining high figure/ground segmentation accuracy.
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
Preview
Unable to display preview. Download preview PDF.
References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. IJCV 1, 321–331 (1988)
Mortensen, E.N., Barrett, W.A.: Interactive segmentation with intelligent scissors. In: Graphical Models and Image Processing, pp. 349–384 (1998)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary amp; region segmentation of objects in n-d images. In: ICCV, pp. 105–112 (2001)
Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: interactive foreground extraction using iterated graph cuts. TOG 23, 309–314 (2004)
Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. TOG 23, 303–308 (2004)
Lempitsky, V., Kohli, P., Rother, C., Sharp, T.: Image segmentation with a bounding box prior. In: ICCV, pp. 277–284 (2009)
Wang, J., Agrawala, M., Cohen, M.F.: Soft scissors: an interactive tool for realtime high quality matting. TOG (2007)
Vicente, S., Kolmogorov, V., Rother, C.: Graph cut based image segmentation with connectivity priors. In: CVPR, pp. 1–8 (2008)
Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive Image Segmentation Using an Adaptive GMMRF Model. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004)
Grady, L.: Random walks for image segmentation. TPAMI 28, 1768–1783 (2006)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. TPAMI 24, 603–619 (2002)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. TPAMI 26, 1124–1137 (2004)
Roy, N., Mccallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: ICML, pp. 441–448 (2001)
Zhu, X., Lafferty, J., Ghahramani, Z.: Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In: ICML Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, pp. 58–65 (2003)
Settles, B.: Active learning literature survey. University of Wisconsin, Madison (2010)
Hoi, S.C.H., Jin, R., Zhu, J., Lyu, M.R.: Batch mode active learning and its application to medical image classification. In: ICML, pp. 417–424 (2006)
Joshi, A., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: CVPR, pp. 2372–2379 (2009)
Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: Interactively co-segmentating topically related images with intelligent scribble guidance. IJCV 93, 273–292 (2011)
Gosselin, P., Cord, M.: Active learning methods for interactive image retrieval. TIP 17, 1200–1211 (2008)
Vijayanarasimhan, S., Grauman, K.: What’s it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations. In: CVPR, pp. 2262–2269 (2009)
Guo, Y., Schuurmans, D.: Discriminative batch mode active learning. In: NIPS, pp. 593–600 (2007)
Grady, L., Schwartz, E.: Isoperimetric graph partitioning for image segmentation. TPAMI 28, 469–475 (2006)
Yang, W., Cai, J., Zheng, J., Luo, J.: User-friendly interactive image segmentation through unified combinatorial user inputs. TIP 19, 2470–2479 (2010)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416–423 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, D., Yan, C., Shan, S., Chen, X. (2013). Active Learning for Interactive Segmentation with Expected Confidence Change. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-37331-2_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37330-5
Online ISBN: 978-3-642-37331-2
eBook Packages: Computer ScienceComputer Science (R0)