A Novel Topic-Level Random Walk Framework for Scene Image Co-segmentation
Conference paper
Abstract
Image co-segmentation is popular with its ability to detour supervisory data by exploiting the common information in multiple images. In this paper, we aim at a more challenging branch called scene image co-segmentation, which jointly segments multiple images captured from the same scene into regions corresponding to their respective classes. We first put forward a novel representation named Visual Relation Network (VRN) to organize multiple segments, and then search for meaningful segments for every image through voting on the network. Scalable topic-level random walk is then used to solve the voting problem. Experiments on the benchmark MSRC-v2, the more difficult LabelMe and SUN datasets show the superiority over the state-of-the-art methods.
Keywords
Image co-segmentation voting random walk link analysis Download
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References
- 1.Choi, M.J., Torralba, A., Willsky, A.S.: A tree-based context model for object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 240–252 (2012)CrossRefGoogle Scholar
- 2.Dai, J., Wu, Y.N., Zhou, J., Zhu, S.C.: Cosegmentation and cosketch by unsupervised learning. In: ICCV (2013)Google Scholar
- 3.Endres, I., Hoiem, D.: Category-independent object proposals with diverse ranking. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 222–234 (2014)CrossRefGoogle Scholar
- 4.Faktor, A., Irani, M.: Co-segmentation by composition. In: ICCV (2013)Google Scholar
- 5.Gould, S., Rodgers, J., Cohen, D., Elidan, G., Koller, D.: Multi-class segmentation with relative location prior. International Journal of Computer Vision 80(3), 300–316 (2008)CrossRefGoogle Scholar
- 6.Hochbaum, D.S., Singh, V.: An efficient algorithm for co-segmentation. In: ICCV, pp. 269–276 (2009)Google Scholar
- 7.Joulin, A., Bach, F., Ponce, J.: Multi-class cosegmentation. In: CVPR, pp. 542–549 (2012)Google Scholar
- 8.Joulin, A., Bach, F.R., Ponce, J.: Discriminative clustering for image co-segmentation. In: CVPR, pp. 1943–1950 (2010)Google Scholar
- 9.Kim, G., Faloutsos, C., Hebert, M.: Unsupervised modeling of object categories using link analysis techniques. In: CVPR (2008)Google Scholar
- 10.Kim, G., Xing, E.P.: On multiple foreground cosegmentation. In: CVPR, pp. 837–844 (2012)Google Scholar
- 11.Kowdle, A., Sinha, S.N., Szeliski, R.: Multiple view object cosegmentation using appearance and stereo cues. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 789–803. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 12.Lee, Y.J., Grauman, K.: Object-graphs for context-aware visual category discovery. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 346–358 (2012)CrossRefGoogle Scholar
- 13.Ma, T., Latecki, L.J.: Graph transduction learning with connectivity constraints with application to multiple foreground cosegmentation. In: CVPR, pp. 1955–1962 (2013)Google Scholar
- 14.Mukherjee, L., Singh, V., Peng, J.: Scale invariant cosegmentation for image groups. In: CVPR, pp. 1881–1888 (2011)Google Scholar
- 15.Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
- 16.Rother, C., Minka, T.P., Blake, A., Kolmogorov, V.: Cosegmentation of image pairs by histogram matching - incorporating a global constraint into mrfs. In: CVPR (1), pp. 993–1000 (2006)Google Scholar
- 17.Rubinstein, M., Joulin, A., Kopf, J., Liu, C.: Unsupervised joint object discovery and segmentation in internet images. In: CVPR, pp. 1939–1946 (2013)Google Scholar
- 18.Rubio, J.C., Serrat, J., López, A.M., Paragios, N.: Unsupervised co-segmentation through region matching. In: CVPR, pp. 749–756 (2012)Google Scholar
- 19.Russell, B.C., Freeman, W.T., Efros, A.A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2), pp. 1605–1614 (2006)Google Scholar
- 20.Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: A database and web-based tool for image annotation. International Journal of Computer Vision 77(1-3), 157–173 (2008)CrossRefGoogle Scholar
- 21.Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
- 22.Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 23.Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2005)Google Scholar
- 24.Vicente, S., Rother, C., Kolmogorov, V.: Object cosegmentation. In: CVPR, pp. 2217–2224 (2011)Google Scholar
- 25.Wang, F., Huang, Q., Guibas, L.J.: Image co-segmentation via consistent functional maps. In: ICCV (2013)Google Scholar
- 26.Yang, Z., Tang, J., Zhang, J., Li, J., Gao, B.: Topic-level random walk through probabilistic model. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, Q.-M. (eds.) APWeb/WAIM 2009. LNCS, vol. 5446, pp. 162–173. Springer, Heidelberg (2009)CrossRefGoogle Scholar
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