Weighted Feature Space Representation with Kernel for Image Classification

Abstract

The kernel method is a very effective and popular method to extract features from data such as images. A novel method is presented to enhance traditional kernel method for face image representation in this paper, which is very suitable to treat the high-dimensional datasets. The proposed method is called weighted kernel representation-based method (WKRBM) in this paper. WKRBM assumes that the test sample can be expressed by all the training samples and linear solution in the mapping space. It uses the obtained linear combination to recognize face images. In particular, the coefficients of a linear combination can be set as the optimal weight that is an important factor to obtain better performance for image classification. The rationale, characteristics, and advantages of the proposed method are presented. The analysis describes that WKRBM outperforms collaborative representation-based kernel method for image recognition. Extensive experimental results illustrate that WKRBM has partial properties of sparsity, which is effective to recognize images.