A Review on Visual Recognition of RGB Images and Videos by Learning from RGB-D Data

Content Provider	Scilit
Author	Jose, Alphonsa Sreejith, V.
Copyright Year	2018
Description	Journal: Iop Conference Series: Materials Science and Engineering Domain adaptation provides the possibility of research that allows changes in data distribution across training and testing datasets. Recognizing the RGB images by learning RGB-D data contains the additional depth information. The unsupervised domain adaptation (UDA) take advantage of the additional depth features. UDA deals with domain mismatch between the source and the target. The various adaptation techniques deals with the source and target domain. The domain mismatch is minimized by describing a projection matrix that is optimized by reducing the Maximum Mean Discrepancy (MMD) and aligning the source and target domains. To optimize the depth information the correlation between different types of features are to be maximized. Inorder to simultaneously cope with the domain mismatch issues, a unified framework called domain adaptation from multi-view to single-view (DAM2S) is learned. The effectiveness of the proposed methods for recognizing RGB images and videos by learning from RGB-D data is demonstrated by comprehensive experiments for object recognition, cross dataset and cross-view action recognition.
Related Links	http://iopscience.iop.org/article/10.1088/1757-899X/396/1/012040/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/396/1/012040
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	1
Volume Number	396
Language	English
Publisher	IOP Publishing
Publisher Date	2018-08-29
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Artificial Intelligence Recognizing Rgb Images Domain Adaptation Videos By Learning
Content Type	Text
Resource Type	Article