MPEG-7 descriptor selection using Localized Generalization Error Model with mutual information

Abstract

MPEG-7 provides a set of descriptors to describe the content of an image. However, how to select or combine descriptors for a specific image classification problem is still an open problem. Currently, descriptors are usually selected by human experts. Moreover, selecting the same set of descriptors for different classes of images may not be reasonable. In this work we propose a MPEG-7 descriptor selection method which selects different MPEG-7 descriptors for different image class in an image classification problem. The proposed method L-GEMIM combines Localized Generalization Error Model (L-GEM) and Mutual Information (MI) to assess the relevance of MPEG-7 descriptors for a particular image class. The L-GEMIM model assesses the relevance based on the generalization capability of a MPEG-7 descriptor using L-GEM and prevents redundant descriptors being selected by MI. Experimental results using 4,000 images in 4 classes show that L-GEMIM selects better set of MPEG-7 descriptors yielding a higher testing accuracy of image classification.