Bilinear discriminant feature line analysis for image feature extraction
This work addresses computational and feature preservation issues in image classification, but it appears incremental as it builds on existing nearest feature line methods by extending them to a matrix-based approach.
The paper tackles the problem of high computational complexity and loss of geometric features in image classification by proposing a matrix-based bilinear discriminant feature line analysis (BDFLA) for feature extraction, which minimizes within-class scatter and maximizes between-class scatter using a two-dimensional nearest feature line, with experimental results confirming its effectiveness on two image databases.
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.