  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.
