# Bilinear discriminant feature line analysis for image feature extraction

Source: [Bilinear discriminant feature line analysis for image feature extraction](<https://www.sota2.com/research/paper/68fa65ba1e209131cea0be09>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:38.835Z

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

Published: 2019-05-03T04:54:43.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1905.03710>)

[PDF](<https://arxiv.org/pdf/1905.03710.pdf>)