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.
@article{arxiv.1905.03710,
title = {Bilinear discriminant feature line analysis for image feature extraction},
author = {Lijun Yan and Jun-Bao Li and Xiaorui Zhu and Jeng-Shyang Pan and Linlin Tang},
journal= {arXiv preprint arXiv:1905.03710},
year = {2019}
}