Can I read Bilinear Discriminant Feature Line Analysis for Image Feature Extraction on EtoBox?

Bilinear Discriminant Feature Line Analysis for Image Feature Extraction by Yan, Lijun; Li, Jun-Bao; Zhu, Xiaorui; Pan, Jeng-Shyang; Tang, Linlin is a scholarly article available to read on EtoBox.

What is Bilinear Discriminant Feature Line Analysis for Image Feature Extraction about?

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.

Author

Yan, Lijun; Li, Jun-Bao; Zhu, Xiaorui; Pan, Jeng-Shyang; Tang, Linlin

Published

2019

Language

EN