iccv iccv2013 iccv2013-383 iccv2013-383-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Zhengxiang Wang, Rujie Liu
Abstract: This paper introduces to use semi-supervised learning for large scale image cosegmentation. Different from traditional unsupervised cosegmentation that does not use any segmentation groundtruth, semi-supervised cosegmentation exploits the similarity from both the very limited training image foregrounds, as well as the common object shared between the large number of unsegmented images. This would be a much practical way to effectively cosegment a large number of related images simultaneously, where previous unsupervised cosegmentation work poorly due to the large variances in appearance between different images and the lack ofsegmentation groundtruthfor guidance in cosegmentation. For semi-supervised cosegmentation in large scale, we propose an effective method by minimizing an energy function, which consists of the inter-image distance, the intraimage distance and the balance term. We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimization problem into sub-problems, and updates each image alternatively to reduce the number of variables in each subproblem for computation efficiency. Experiment results on iCoseg and Pascal VOC datasets show that the proposed cosegmentation method can effectively cosegment hundreds of images in less than one minute. And our semi-supervised cosegmentation is able to outperform both unsupervised cosegmentation as well asfully supervised single image segmentation, especially when the training data is limited.
[1] D. Batra, A. Kowdle, D. Parikh, J. Luo, and T. Chen. icoseg: Interactive co-segmentation with intelligent scribble guidance. In CVPR, 2010.
[2] A. Billionnet and S. Elloumi. Using a mixed integer quadratic programming solver for the unconstrained quadratic 0-1 problem. Mathematical Programming, 109(1), 2007.
[3] Y. Chai, V. Lempitsky, and A. Zisserman. Bicos: A bi-level co-segmentation method for image classification. In ICCV, 2011.
[4] Y. Chai, E. Rahtu, V. Lempitsky, L. Van Gool, and A. Zisserman. Tricos: A tri-level class-discriminative cosegmentation method for image classification. In ECCV, 2012.
[5] K.-Y. Chang, T.-L. Liu, and S.-H. Lai. From co-saliency to co-segmentation: An efficient and fully unsupervised energy minimization model. In CVPR, 2011.
[6] O. Chapelle, B. Sch o¨lkopf, A. Zien, et al. Semi-supervised learning, volume 2. MIT press Cambridge, 2006.
[7] J. Cui, Q. Yang, F. Wen, Q. Wu, C. Zhang, L. Van Gool, and X. Tang. Transductive object cutout. In CVPR, 2008.
[8] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20] Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. M. Guillaumin, J. Verbeek, and C. Schmid. Multimodal semi-supervised learning for image classification. In CVPR, 2010. D. S. Hochbaum and V. Singh. An efficient algorithm for co-segmentation. In ICCV, 2009. S. Hoi, W. Liu, and S.-F. Chang. Semi-supervised distance metric learning for collaborative image retrieval. In CVPR, 2008. A. Joulin, F. Bach, and J. Ponce. Discriminative clustering for image co-segmentation. In CVPR, 2010. A. Joulin, F. Bach, and J. Ponce. Multi-class cosegmentation. In CVPR, 2012. E. Kim, H. Li, and X. Huang. A hierarchical image clustering cosegmentation framework. In CVPR, 2012. G. Kim and E. P. Xing. On multiple foreground cosegmentation. In CVPR, 2012. G. Kim, E. P. Xing, L. Fei-Fei, and T. Kanade. Distributed cosegmentation via submodular optimization on anisotropic diffusion. In ICCV, 2011. D. Kuettel, M. Guillaumin, and V. Ferrari. Segmentation propagation in imagenet. In ECCV, 2012. Y. Mu and B. Zhou. Co-segmentation of image pairs with quadratic global constraint in mrfs. In ACCV, 2007. L. Mukherjee, V. Singh, and C. R. Dyer. Half-integrality based algorithms for cosegmentation of images. In CVPR, 2009.
[21] L. Mukherjee, V. Singh, and J. Peng. Scale invariant cosegmentation for image groups. In CVPR, 2011.
[22] L. Mukherjee, V. Singh, J. Xu, and M. D. Collins. Analyzing the subspace structure of related images: concurrent segmentation of image sets. In ECCV, 2012.
[23] C. Olsson, A. P. Eriksson, and F. Kahl. Solving large scale binary quadratic problems: Spectral methods vs. semidefinite programming. In CVPR, 2007.
[24] C. Rother, T. Minka, A. Blake, and V. Kolmogorov. Cosegmentation of image pairs by histogram matchingincorporating a global constraint into mrfs. In CVPR, 2006.
[25] J. C. Rubio, J. Serrat, A. L ´opez, and N. Paragios. Unsupervised co-segmentation through region matching. In CVPR, 2012.
[26] S. Vicente, V. Kolmogorov, and C. Rother. Cosegmentation revisited: Models and optimization. In ECCV, 2010.
[27] S. Vicente, C. Rother, and V. Kolmogorov. Object cosegmentation. In CVPR, 2011.
[28] X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison, 2005. 400