Using Score Distribution Models to Select the Kernel Type for a Web-Based Adaptive Image Retrieval System (AIRS)

摘要

The goal of this paper is to investigate the selection of the kernel for a Web-based AIRS. Using the Kernel Rocchio learning method, several kernels having polynomial and Gaussian forms are applied to general images represented by color histograms in RGB and HSV color spaces. Experimental results on these collections show that performance varies significantly between different kernel types and that choosing an appropriate kernel is important. Then, based on these results, we propose a method for selecting the kernel type that uses the score distribution models. Experimental results on our data show that the proposed method is effective for our system.