LDA Versus MMD Approximation on Mislabeled Images for Dependant Selection of Visual Features and Their Heterogeneity

We propose first to generate new visual features based on entropy measure (heterogeneity), and then we address the question of feature selection in the context of mislabeled images for automatic image classification. We compare two methods of word dependant feature selection on mislabeled images: approximation of linear discriminant analysis (ALDA) and approximation of maximum marginal diversity (AMMD). A hierarchical ascendant classification (HAC) is trained and tested using full or reduced visual space. Experiments are conducted on 10 K Corel images with 52 keywords, 40 visual features (U) and 40 new heterogeneity features (H). Compared to HAC on all U features, we measure a classification gain of 56% and in the same time a reduction of 92% of the number of features using a simple late fusion of U and H