Abstract
In this chapter, we describe in detail our approach to interactive cosegmentation. We formulate the task as an energy minimization problem across all related images in a group. The energies across images are tied together via a shared appearance model, thus allowing for efficient inference. After describing our formulation, we present an active learning approach that makes efficient use of users’ time. A wide variety of cues are combined to intelligently guide the users’ next scribbles. We then introduce our co-segmentation dataset, The CMU-Cornell iCoseg dataset, the largest of its kind to date. We evaluate our system on this dataset using machine simulations as well as real user-studies.We find that our approach can achieve comparable co-segmentation performance with less user effort.
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References
Bagon, S.: Matlab Wrapper for Graph Cut. http://www.wisdom.weizmann.ac.il/Ëśbagon (2006).
Batra, D., Kowdle, A., Parikh, D., Tang, K., Chen, T.: Interactive Cosegmentation by Touch.http://amp.ece.cornell.edu/projects/touch-coseg/ (2009).
Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance. International Journal of Computer Vision (2011).
Batra, D., Parikh, D., Kowdle, A., Chen, T., Luo, J.: Seed Image Selection in Interactive Cosegmentation. International Conference on Image Processing (2009).
Batra, D., Sukthankar, R., Chen, T.: Semi-Supervised Clustering via Learnt Codeword Distances. British Machine Vision Conference (2008).
Bouman, C.A.: Cluster: An unsupervised algorithm for modeling Gaussian mixtures. Available from http://www.ece.purdue.edu/\stringËśbouman. (1997).
Boykov, Y.Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. International Conference on Computer Vision (2001).
Boykov, Y., Kolmogorov, V.: An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004).
Boykov, Y., Veksler, O., Zabih, R.: Efficient Approximate Energy Minimization via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence (2001).
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (2002).
Criminisi, A., Sharp, T., Blake, A.: GeoS: Geodesic Image Segmentation. European Conference on Computer Vision (2008).
Cui, J., Yang, Q.,Wen, F.,Wu, Q., Zhang, C,. Gool, L.V., Tang, T.: Transductive Object Cutout. IEEE Conference on Computer Vision and Pattern Recognition (2008).
Hochbaum, D.S., Singh, V.: An efficient algorithm for co-segmentation. International Conference on Computer Vision (2009).
Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. International Conference on Computer Vision (2005).
Kohli, P., Philip, T.H,S: Measuring uncertainty in graph cut solutions. Elsevier Journal on Computer Vision and Image Understanding (2008).
Kolmogorov, V., Zabih, R.: What Energy Functions can be Minimized via Graph Cuts?. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004).
Leung, T., Malik, J.: Contour continuity in region based image segmentation. European Conference on Computer Vision (1998).
Mu, Y.D., Zhou, B.F.: Co-segmentation of Image Pairs with Quadratic Global Constraint in MRFs. Asian Conference on Computer Vision (2007).
Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. IEEE Conference on Computer Vision and Pattern Recognition (2009).
Oliva, A., Torralba, A.: Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. International Journal of Computer Vision (2001).
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics (Proceedings of SIGGRAPH) (2004).
Rother, C., Minka, T., Blake, A., Kolmogorov, V., Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs (2006)
Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. Computational Learning Theory (1992).
Schnitman, Y., Caspi, Y., Cohen O.D.. Lischinski, D.: Inducing Semantic Segmentation from an Example. Asian Conference on Computer Vision (2006).
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Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T. (2011). An Approach to Interactive Co-segmentation. In: Interactive Co-segmentation of Objects in Image Collections. SpringerBriefs in Computer Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1915-0_2
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DOI: https://doi.org/10.1007/978-1-4614-1915-0_2
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