Buried object detection using handheld WEMI with task-driven extended functions of multiple instances

TL;DR

This paper introduces a novel multiple instance dictionary learning algorithm for buried object detection using handheld WEMI sensors, effectively handling imprecise labels and improving detection accuracy.

Contribution

It proposes the Task Driven Extended Functions of Multiple Instances algorithm, enabling discriminative dictionary learning without precise point-wise labels in buried object detection.

Findings

Effective detection on measured WEMI data

Handles imprecise training labels

Improves discriminative dictionary learning

Abstract

Many effective supervised discriminative dictionary learning methods have been developed in the literature. However, when training these algorithms, precise ground-truth of the training data is required to provide very accurate point-wise labels. Yet, in many applications, accurate labels are not always feasible. This is especially true in the case of buried object detection in which the size of the objects are not consistent. In this paper, a new multiple instance dictionary learning algorithm for detecting buried objects using a handheld WEMI sensor is detailed. The new algorithm, Task Driven Extended Functions of Multiple Instances, can overcome data that does not have very precise point-wise labels and still learn a highly discriminative dictionary. Results are presented and discussed on measured WEMI data.