Two-directional two-dimensional discriminant locality preserving projections for image recognition
We propose in this paper an improved manifold learning method called two-directional two-dimensional discriminant locality preserving projections, (2D)2-DLPP, for efficient image recognition. As the existing method of two-dimensional discriminant locality preserving projections (2D-DLPP) mainly relies upon the local structure information in the rows of images, we first derive an alternative 2D-DLPP algorithm that makes use of the information in the columns. Exploiting the local structure and discriminant information in both the rows and the columns, we develop the (2D)2-DLPP method for efficient image feature extraction and dimensionality reduction. Experimental results on two benchmark image datasets show the effectiveness of the proposed method.