NDLI: Quaternion softmax classifier

Content Provider	IET Digital Library
Author	Zeng, Rui Wu, Jiasong Shao, Zhuhong Senhadji, Lotfi Shu, Huazhong
Abstract	For the feature extraction of red–blue–green (RGB) colour images, researchers usually deal with R, G and B channels separately to obtain three feature vectors, and then combine them together to obtain a long real feature vector. This approach does not exploit the relationships between the three channels of the colour images. Recently, attention has been paid to quaternion features, which take the relationships between channels into consideration and seem to be more suitable for representing colour images. However, there are only a few quaternion classifiers for dealing with quaternion features. To meet this requirement, a new quaternion classifier, namely, the quaternion softmax classifier is proposed, which is an extended version of the conventional softmax classifier generally defined in the complex (or real) domain. The proposed quaternion softmax classifier is applied to two of the most common quaternion features, that is, the quaternion principal components analysis feature and the colour image pixel feature. The experimental results show that the proposed method performs better than the quaternion back propagation neural network in terms of accuracy and convergence rate.
Starting Page	1929
Ending Page	1931
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.2526
Journal	Electronics Letters
Publisher Date	2014-11-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	B Channel Colour Image Pixel Feature Colour Image Representation Computer Vision And Image Processing Technique Feature Extraction Feature Vector G Channel Image Classification Image Colour Analysis Image Recognition Image Representation Principal Component Analysis QPCA Feature Quaternion Feature Quaternion Principal Component Analysis Feature Quaternion Softmax Classifier R Channel Red-bluegreen Colour Image RGB Colour Image Statistics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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