NDLI: Supervised orthogonal discriminant projection based on double adjacency graphs for image classification

Content Provider	IET Digital Library
Author	Wang, Bangjun Zhang, Li Li, Fanzhang
Abstract	This study proposes a supervised orthogonal discriminant projection (SODP) based on double adjacency graphs (DAGs). SODP based on DAG (SODP-DAG) aims to minimise the local within-class scatter and simultaneously maximise both the local between-class scatter and the non-local scatter, where the local between-class scatter and the local within-class scatter are constructed by applying the DAG structure. By doing so, SODP-DAG can keep the local within-class structure for original data and find the optimal discriminant directions effectively. Moreover, four schemes are designed for constructing weight matrices in SODP-DAG. To validate the performance of SODP-DAG, the authors compared it with orthogonal discriminant projection, SODP and others on several publicly available datasets. Experimental results show the feasibility and effectiveness of SODP-DAG.
Starting Page	1050
Ending Page	1058
Page Count	9
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 11, Nov (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0160
Journal	IET Image Processing
Publisher Date	2017-05-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Combinatorial Mathematics Computer Vision And Image Processing Technique Double Adjacency Graphs Graph Theory Image Classification Image Recognition Local Between-class Scatter Local Within-class Scatter Minimisation Local Within-class Structure Matrix Algebra Minimisation Nonlocal Scatter Optimal Discriminant Directions Optimisation Technique SODP-DAG Supervised Orthogonal Discriminant Projection Weight Matrix
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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