Abstract
Feature extraction and selection has always been an interesting issue for pattern recognition tasks. There have been numerous feature schemes proposed and empirically validated for image scene and object categorization problems, no matter it is for general-purposed applications such as image retrieval, or for specific domains such as medical image analysis. On the other hand, there are few attempts in assessing the effectiveness of these features using machine learning methods of feature analysis. We review some recent advances in feature selection and investigate the use of feature analysis and selection in two case studies. Our aim is to demonstrate that feature selection is indispensable in providing clues for finding good feature combination schemes and building compact and effective classifiers that produce much improved performance.