Bilinear discriminant feature line analysis for image feature extraction

TL;DR

This paper introduces a matrix-based bilinear discriminant feature line analysis method for image feature extraction, reducing computational complexity and preserving geometric features, showing promising results on image databases.

Contribution

It presents a novel 2D NFL-based algorithm that avoids vectorization, improving efficiency and feature preservation in image classification tasks.

Findings

Effective in reducing computational complexity

Preserves geometric features of images

Shows improved classification performance

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.