Bilinear discriminant feature line analysis for image feature extraction. Issue 4 (1st February 2015)

• Record Type: Journal Article
• Authors: Yan, Lijun; Li, Jun‐Bao; Zhu, Xiaorui; Pan, Jeng‐Shyang; Tang, Linlin
• Is Part Of: Electronics letters. Volume 51:Issue 4(2015)
• Page Start: 336
• Page End: 338
• Publication Date: 2015-02-01
• Subjects: feature extraction -- image classification -- matrix algebra; bilinear discriminant feature line analysis -- BDFLA -- image feature extraction -- nearest feature line -- NFL‐based subspace algorithms -- image classification task -- image samples -- matrix‐based algorithm -- within‐class scatter -- between‐class scatter -- two‐dimensional NFL -- 2D NFL -- two‐image databases; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2014.3834
• Languages: English
• ISSNs: 0013-5194

Abstract

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.