Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images
Författare
Summary, in English
We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many visual categories and instances. Our approach is based on a parametric figureground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image followed by a sequential inference algorithm that produces a complete scene estimate. Our contributions can be summarized as follows: (1) a generalization of parametric max flow figure-ground proposal methodology to take advantage of intensity and depth information, in order to systematically and efficiently generate the breakpoints of an underlying spatial model in polynomial time, (2) new region description methods based on second-order pooling over multiple features constructed using both intensity and depth channels, (3) a principled search-based structured prediction inference and learning process that resolves conflicts in overlapping spatial partitions and selects regions sequentially towards complete scene estimates, and (4) extensive evaluation of the impact of depth, as well as the effectiveness of a large number of descriptors, both pre-designed and automatically obtained using deep learning, in a difficult RGB-D semantic segmentation problem with 92 classes. We report state of the art results in the challenging NYU Depth Dataset V2 [44], extended for the RMRC 2013 and RMRC 2014 Indoor Segmentation Challenges, where currently the proposed model ranks first. Moreover, we show that by combining second-order and deep learning features, over 15% relative accuracy improvements can be additionally achieved. In a scene classification benchmark, our methodology further improves the state of the art by 24%.
Publiceringsår
2015-10-14
Språk
Engelska
Sidor
3517-3526
Publikation/Tidskrift/Serie
IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Dokumenttyp
Konferensbidrag
Förlag
IEEE Computer Society
Ämne
- Computer Vision and Robotics (Autonomous Systems)
Conference name
IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Conference date
2015-06-07 - 2015-06-12
Conference place
Boston, United States
Status
Published
ISBN/ISSN/Övrigt
- ISBN: 9781467369640