24 December 2013 Semantic labeling of indoor scenes from RGB-D images with discriminative learning
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Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90670C (2013) https://doi.org/10.1117/12.2049805
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom
Abstract
Recently emerged RGB-D sensors provide great promise for indoor scene understanding, which is a fundamental and challenging problem in computer vision. We present a discriminative model in this paper to semantically label indoor scenes from RGB-D images. Unlike previous work which only labels pre-determined superpixels, we characterize the scenes with a set of planes and compose them into objects. The optimal way to composition and corresponding labels are inferred simultaneously using a greedy algorithm. Our model considers unary features and pairwise and co-occurrence context, as well as latent variables that account for multi-mode distributions of each object category. We train the model with latent structural SVM learning framework. Our approach achieves state-of-the-art performance on the Cornell RGB-D indoor scene dataset [1].
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Bo Liu, Haoqi Fan, "Semantic labeling of indoor scenes from RGB-D images with discriminative learning", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670C (24 December 2013); doi: 10.1117/12.2049805; https://doi.org/10.1117/12.2049805
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