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A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

An Empirical Analysis of Deep Feature Learning for RGB-D Object Recognition

Authors : Ali Caglayan, Ahmet Burak Can

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Conventional deep feature learning methods use the same model parameters for both RGB and depth domains in RGB-D object recognition. Since the characteristics of RGB and depth data are different, the suitability of such approaches is suspicious. In this paper, we empirically investigate the effects of different model parameters on RGB and depth domains using the Washington RGB-D Object Dataset. We have explored the effects of different filter learning approaches, rectifier functions, pooling methods, and classifiers for RGB and depth data separately. We have found that individual model parameters fit best for RGB and depth data.

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Footnotes
1
In fact, there are 207.920 images in total, but 258 of them do not have object mask.
 
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Metadata
Title
An Empirical Analysis of Deep Feature Learning for RGB-D Object Recognition
Authors
Ali Caglayan
Ahmet Burak Can
Copyright Year
2017
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-59875-8

Electronic ISBN: 978-3-319-59876-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59876-5_35

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