Multiple local kernel integrated feature selection for image classification

Feature redundancy and loss of local feature are central problems for image classification. Feature selection decreases the feature redundancy by choosing a subset of features and eliminating those with low prediction. The local feature representation is able to highlight objects in an image, thus, overcoming the drawbacks of global features. This paper presents a new method, called the local kernel for feature selection, which integrates a local kernel of the segmentation regions into feature selection to provide improved image classification, by means of the region-based image distance integrated into the kernel of the Bayesian classifier. The proposed method is tested on two standard image databases and the classification results are higher than the current feature selection and classification methods.