TY - GEN
T1 - Discriminative learning with latent variables for cluttered indoor scene understanding
AU - Wang, Huayan
AU - Gould, Stephen
AU - Koller, Daphne
PY - 2010
Y1 - 2010
N2 - 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. [4] that requires clutter labels.
AB - 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. [4] that requires clutter labels.
UR - http://www.scopus.com/inward/record.url?scp=78149327185&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15561-1_36
DO - 10.1007/978-3-642-15561-1_36
M3 - Conference contribution
SN - 364215560X
SN - 9783642155604
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 497
EP - 510
BT - Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
PB - Springer Verlag
T2 - 11th European Conference on Computer Vision, ECCV 2010
Y2 - 10 September 2010 through 11 September 2010
ER -