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2016 | OriginalPaper | Buchkapitel

Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks

verfasst von : Jinghua Wang, Zhenhua Wang, Dacheng Tao, Simon See, Gang Wang

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We take advantage of deconvolutional networks which can predict pixel-wise class labels, and develop a new structure for deconvolution of multiple modalities. We propose a novel feature transformation network to bridge the convolutional networks and deconvolutional networks. In the feature transformation network, we correlate the two modalities by discovering common features between them, as well as characterize each modality by discovering modality specific features. With the common features, we not only closely correlate the two modalities, but also allow them to borrow features from each other to enhance the representation of shared information. With specific features, we capture the visual patterns that are only visible in one modality. The proposed network achieves competitive segmentation accuracy on NYU depth dataset V1 and V2.

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Titel: Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks
verfasst von: Jinghua Wang
Zhenhua Wang
Dacheng Tao
Simon See
Gang Wang
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46453-4

Electronic ISBN: 978-3-319-46454-1

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46454-1_40

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