Low‐rank preserving embedding regression for robust image feature extraction

Low‐rank preserving embedding regression for robust image feature extraction

Abstract Although low‐rank representation (LRR)‐based subspace learning has been widely applied for feature extraction in computer vision, how to enhance the discriminability of the low‐dimensional features extracted by LRR based subspace learning methods is still a problem that needs to be further...

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Bibliographic Details
Main Authors: Tao Zhang, Chen‐Feng Long, Yang‐Jun Deng, Wei‐Ye Wang, Si‐Qiao Tan, Heng‐Chao Li
Format: Article
Language:English
Published: Wiley 2024-02-01
Series:IET Computer Vision
Subjects:
feature extraction
image representation
pattern recognition
Online Access:https://doi.org/10.1049/cvi2.12228

Internet

https://doi.org/10.1049/cvi2.12228

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