A new locally linear KNN method with an improved marginal Fisher analysis for image classification

This paper presents a novel locally linear KNN method with an improved marginal Fisher analysis for image classification. First, the discriminating color space (DCS), which is derived by discriminant analysis of the red, green, and blue primary colors, is integrated into the proposed method. Second, an improved marginal Fisher analysis (IMFA) applies an eigenvalue spectrum analysis to improve the generalization performance of the marginal Fisher analysis method. Third, a new locally linear KNN classifier (LLKNN), which represents the test image as a linear combination of its k nearest training images and assigns it to the class with the largest sum of weights, is presented to improve upon the traditional KNN approach. The effectiveness of the proposed method is evaluated on two representative datasets, namely the AR face image data set and the ETH-80 image data set. Experimental results show that the proposed method performs better than some representative state-of-the-art methods.