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Bilinear discriminant feature line analysis for image feature extraction
May 03, 2019
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.
Polyhedron Volume-Ratio-based Classification for Image Recognition
Jan 26, 2016
In this paper, a novel method, called polyhedron volume ratio classification (PVRC) is proposed for image recognition