Discriminative Learning with Latent Variables for Cluttered Indoor Scene Understanding
We address the problem of understanding an indoor scene from a single image in terms of recovering the layouts of the faces (floor, ceiling, walls) and furniture. A major challenge of this task arises from the fact that most indoor scenes are cluttered by furniture and decorations, whose appearances vary drastically across scenes, and can hardly be modeled (or even hand-labeled) consistently. In this paper we tackle this problem by introducing latent variables to account for clutters, so that the observed image is jointly explained by the face and clutter layouts. Model parameters are learned in the maximum margin formulation, which is constrained by extra prior energy terms that define the role of the latent variables. Our approach enables taking into account and inferring indoor clutter layouts without hand-labeling of the clutters in the training set. Yet it outperforms the state-of-the-art method of Hedau et al.  that requires clutter labels.
KeywordsLatent Variable Prior Constraint Inference Method Indoor Scene Major Face
Unable to display preview. Download preview PDF.
- 1.Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on PAMI 24(5) (2002)Google Scholar
- 2.Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Transactions on PAMI (to appear, 2010)Google Scholar
- 3.Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: ICCV (2009)Google Scholar
- 4.Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered room. In: ICCV 2009 (2009)Google Scholar
- 6.Hoiem, D., Efros, A., Hebert, M.: Recovering surface layout from an image. IJCV 75(1) (2007)Google Scholar
- 8.Rother, C.: A new approach to vanishing point detection in architectural environments. IVC 20 (2002)Google Scholar
- 9.Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. IJCV (2007)Google Scholar
- 10.Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y., Singer, Y.: Large margin methods for structured and interdependent output variables. JMLR 6, 1453–1484 (2005)Google Scholar
- 11.Vedaldi, A., Zisserman, A.: Structured output regression for detection with partial occlusion. In: NIPS (2009)Google Scholar
- 12.Yu, C.-N., Joachims, T.: Learning structural SVMs with latent variable. In: ICML (2009)Google Scholar