Multiple Instance Dictionary Learning using Functions of Multiple Instances

Abstract

Dictionary Learning Functions of Multiple Instances (DL-FUMI) is proposed to address target detection problems with inaccurate training labels. DL-FUMI is a multiple instance dictionary learning method that estimates target atoms that describe distinctive and representative features of the target class and background atoms that account for the shared features found across both target and non-target data points. Experimental results show that the target atoms estimated by DL-FUMI are more discriminative and representative of the target class than comparison methods. DL-FUMI is shown to have improved performance on several detection problems as compared to other multiple instance dictionary learning algorithms.