Year: 2015
Type: article
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.
Source: Electronics Letters
DOI: 10.1049/el.2014.3834
Institution: Harbin Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2017194723