Kernel method for improving image retrieval performance: a survey

The subject of this paper is the kernel method, which offers an elegant solution to improve the effectiveness and the efficiency of the learning process in an image retrieval system. By simulating learning in high-dimensional feature spaces while working with the original low-dimensional input data, kernel methods provide a way for obtaining non-linear decision boundaries from algorithms previously restricted to handling only linearly separable image collections. The aim of this paper is to summarise results that have been obtained by using the kernel method in image retrieval systems. First, the paper introduces the kernel method together with some of its useful properties, and several kernel types used in fields like image retrieval, text mining, machine learning, and computational biology. Then, the paper focuses on applications and research questions involving the kernel method, such as kernel-based learning, kernel selection and feature selection methods.