Multi-label ReliefF and F-statistic feature selections for image annotation

The classical ReliefF and F-statistic feature selections can not be directly applied into multi-label problems due to the ambiguity produced from a data point attributed to multiple classes simultaneously. In this paper, we present MReliefF and MF-statistic algorithms for multi-label feature selections. Discriminant features are selected to boost the multi-label classification accuracy. The proposed MReliefF and MF-statistic can be used in image categorization and annotation problems. Extensive experiments on image annotation tasks show the good performance of our approach. To our knowledge, this is the first work to generalize the ReliefF and F-statistic feature selection algorithms for multi-label image annotation tasks.