Multiple Instance Dictionary Learning using Functions of Multiple Instances

TL;DR

This paper introduces DL-FUMI, a multiple instance dictionary learning method that effectively handles inaccurate labels, learning representative target and nontarget features for improved target detection and classification.

Contribution

DL-FUMI is a novel approach that learns more accurate and discriminative target and nontarget dictionaries under label uncertainty, outperforming existing MIL methods.

Findings

DL-FUMI produces more representative target prototypes.

DL-FUMI outperforms existing MIL dictionary learning algorithms.

DL-FUMI achieves significantly better detection and classification accuracy.

Abstract

A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distinctive and representative features of the true positive class as well as a set of nontarget dictionary atoms that account for the shared information found in both the positive and negative instances. Experimental results show that the estimated target dictionary atoms found by DL-FUMI are more representative prototypes and identify better discriminative features of the true positive class than existing methods in the literature. DL-FUMI is shown to have significantly better performance on several target detection and classification problems as compared to other multiple instance learning (MIL) dictionary learning algorithms on a variety of MIL problems.