Interactive Image Feature Selection Aided by Dimensionality Reduction

Abstract

Feature selection is an important step in designing image classification systems. While many automatic feature selection methods exist, most of them are opaque to their users. We consider that users should be able to gain insight into how observations behave in the feature space, since this may allow the design of better features and the incorporation of domain knowledge. For this purpose, we propose a methodology for interactive and iterative selection of image features aided by dimensionality reduction plots and complementary exploration tools. We evaluate our proposal on the problem of feature selection for skin lesion image classification.