iccv iccv2013 iccv2013-95 iccv2013-95-reference knowledge-graph by maker-knowledge-mining
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Author: Jifeng Dai, Ying Nian Wu, Jie Zhou, Song-Chun Zhu
Abstract: Cosegmentation refers to theproblem ofsegmenting multiple images simultaneously by exploiting the similarities between the foreground and background regions in these images. The key issue in cosegmentation is to align common objects between these images. To address this issue, we propose an unsupervised learning framework for cosegmentation, by coupling cosegmentation with what we call “cosketch ”. The goal of cosketch is to automatically discover a codebook of deformable shape templates shared by the input images. These shape templates capture distinct image patterns and each template is matched to similar image patches in different images. Thus the cosketch of the images helps to align foreground objects, thereby providing crucial information for cosegmentation. We present a statistical model whose energy function couples cosketch and cosegmentation. We then present an unsupervised learning algorithm that performs cosketch and cosegmentation by energy minimization. Experiments show that our method outperforms state of the art methods for cosegmentation on the challenging MSRC and iCoseg datasets. We also illustrate our method on a new dataset called Coseg-Rep where cosegmentation can be performed within a single image with repetitive patterns.
[1] N. Ahuja and S. Todorovic. Extracting texels in 2.1D natural textures. In ICCV, 2007. 2 13 11 ?????????????????????????????? ? ? F? ig?ur?e 3?. Some? cosketchandcosegmntaion? ex?am? p?le s? in? t?he? MSRC,i osegandCoseg?-Repdats?ets?. ? ? ? ? ? TabICAlemc 3s2g1.eInt78r65s.2piet4Rv6c98o1rBgs.n0liaufe7-ov86249.reCmla1-u4n8i79o.6tmarC3ns891c4.oernl2Ca3sib4815(9.DA7er0c8145.9eortsD)30an84d1ylfogarD.08c9426e.rgEtc3l9y018p.5Fkinera20b891l5e.aFnd7p89i64xeon3mtgrF.7el4a820t.gorF54is89(3A7.nar1eGmuc62901).5hirtsO8of7912h.3emoslbraPp48o21.97ngeiPos48617d.lu5gaeSp6930r.2e1oatsSc893h15.atrolceniS936t52h.0elwonSc79s312.8oipmacethW.9g-8R3154.e9tsabdliW0p697a.egrvAt42s.
[2] B. Alexe, T. Deselaers, and V. Ferrari. Classcut for unsupervised class segmentation. In ECCV, 2010. 2
[3] D. Batra, A. Kowdle, D. Parikh, J. Luo, and T. Chen. iCoseg: Interactive co-segmentation with intelligent scribble guidance. In CVPR, 2010. 2, 6
[4] Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 23(1 1): 1222–1239, 2001. 2, 5
[5] D. Cremers, T. Kohlberger, and C. Schn o¨rr. Nonlinear shape statistics in Mumford-Shah based segmentation. In ECCV, 2002. 2
[6] Y. Hong, Z. Si, W. Hu, S.-C. Zhu, and Y. N. Wu. Unsupervised learning of compositional sparse code for natural image representation. Q. Appl. Math., in press. 3
[7] A. Joulin, F. Bach, and J. Ponce. Discriminative clustering for image co-segmentation. In CVPR, 2010. 2, 6, 7
[8] A. Joulin, F. Bach, and J. Ponce. Multi-class cosegmentation. In CVPR, 2012. 2, 6, 7
[9] G. Kim, E. P. Xing, L. Fei-Fei, and T. Kanade. Distributed cosegmentation via submodular optimization on anisotropic diffusion. In ICCV, 2011. 2, 6, 7
[10] D. Kuettel, M. Guillaumin, and V. Ferrari. Segmentation propagation in imagenet. In ECCV, 2012. 2
[11] M. Kumar, P. H. Torr, and A. Zisserman. Objcut: Efficient segmentation using top-down and bottom-up cues. PAMI, 32(3):530–545, 2010. 2
[12] N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. Soares. Leafsnap: A computer vision system for automatic plant species identification. In ECCV, 2012. 7
[13] L. Lin, X. Liu, and S.-C. Zhu. Layered graph matching with composite cluster sampling. PAMI, 32(8): 1426–1442, 2010. 2
[14] L. Mukherjee, V. Singh, and J. Peng. Scale invariant cosegmentation for image groups. In CVPR, 2011. 2, 6, 7
[15] B. Packer, S. Gould, and D. Koller. A unified contour-pixel model for figure-ground segmentation. In ECCV, 2010. 2
[16] C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. In TOG, 2004. 7
[17] C. Rother, T. Minka, A. Blake, and V. Kolmogorov. Cosegmentation of image pairs by histogram matchingincorporating a global constraint into MRFs. In CVPR, 2006. 2
[18] M. Rousson and D. Cremers. Efficient kernel density estimation of shape and intensity priors for level set segmentation. In MICCAI, 2005. 2
[19] J. C. Rubio, J. Serrat, A. L ´opez, and N. Paragios. Unsupervised co-segmentation through region matching. In CVPR,
[20]
[21]
[22]
[23] 2012. 2, 7 J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In ECCV, 2006. 2, 6 S. Vicente, C. Rother, and V. Kolmogorov. Object cosegmentation. In CVPR, 2011. 2, 7 J. Winn and N. Jojic. Locus: Learning object classes with unsupervised segmentation. In ICCV, 2005. 2 Y. N. Wu, Z. Si, H. Gong, and S.-C. Zhu. Learning active basis model for object detection and recognition. IJCV, 90(2): 198–235, 2010. 2, 3 13 12