Open AccessBilinear discriminant feature line analysis for image feature extraction
Author(s) -
Yan Lijun,
Li JunBao,
Zhu Xiaorui,
Pan JengShyang,
Tang Linlin
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.3834
Subject(s) - pattern recognition (psychology) , bilinear interpolation , subspace topology , feature extraction , linear discriminant analysis , artificial intelligence , contextual image classification , feature (linguistics) , classifier (uml) , scatter matrix , computer science , image (mathematics) , feature vector , discriminant , k nearest neighbors algorithm , mathematics , computer vision , algorithm , covariance matrix , linguistics , philosophy , estimation of covariance matrices
A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL‐based subspace algorithms were introduced recently. In most of the classical NFL‐based subspace learning approaches, the input samples are vectors. For image classification tasks, the image samples should be transformed to vectors first. This process induces a high computational complexity and may also lead to loss of the geometric feature of samples. The proposed BDFLA is a matrix‐based algorithm. It aims to minimise the within‐class scatter and maximise the between‐class scatter based on a two‐dimensional (2D) NFL. Experimental results on two‐image databases confirm the effectiveness.