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A novel bilinear discriminant feature line analysis (BDFLA) is proposed forimage feature extraction. The nearest feature line (NFL) is a powerfulclassifier. Some NFL-based subspace algorithms were introduced recently. Inmost of the classical NFL-based subspace learning approaches, the input samplesare vectors. For image classification tasks, the image samples should betransformed to vectors first. This process induces a high computationalcomplexity and may also lead to loss of the geometric feature of samples. Theproposed BDFLA is a matrix-based algorithm. It aims to minimise thewithin-class scatter and maximise the between-class scatter based on atwo-dimensional (2D) NFL. Experimental results on two-image databases confirmthe effectiveness.

Bilinear discriminant feature line analysis for image feature extraction