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A Competitive Combat Strategy and Tactics in RTS Games AI and StarCraft Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Indoor Scene Classification by Incorporating Predicted Depth Descriptor

Authors : Yingbin Zheng, Jian Pu, Hong Wang, Hao Ye

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Depth cue is crucial for perception of spatial layout and understanding the cluttered indoor scenes. However, there is little study of leveraging depth information within the image scene classification systems, mainly because the lack of depth labeling in existing monocular image datasets. In this paper, we introduce a framework to overcome this limitation by incorporating the predicted depth descriptor of the monocular images for indoor scene classification. The depth prediction model is firstly learned from existing RGB-D dataset using the multiscale convolutional network. Given a monocular RGB image, a representation encoding the predicted depth cue is generated. This predicted depth descriptors can be further fused with features from color channels. Experiments are performed on two indoor scene classification benchmarks and the quantitative comparisons demonstrate the effectiveness of proposed scheme.

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Footnotes
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Metadata
Title
Indoor Scene Classification by Incorporating Predicted Depth Descriptor
Authors
Yingbin Zheng
Jian Pu
Hong Wang
Hao Ye
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_2