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 recentl...
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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-basedsubspace 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.
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