ABSTRACT Many e ective 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 veryaccurate point-wise labels. Yet, in many applications, accurate labels are not always feasible. This is especiallytrue in the case of buried object detection in which the size of the objects are not consistent. In this paper, anew multiple instance dictionary learning algorithm for detecting buried objects using a handheld WEMI sensoris detailed. The new algorithm, Task Driven Extended Functions of Multiple Instances, can overcome datathat does not have very precise point-wise labels and still learn a highly discriminative dictionary. Results arepresented and discussed on measured WEMI data.Keywords: Supervised Dictionary Learning, Landmine Detection, Electromagnetic Induction, Extended Func-tions of Multiple Instances 1. INTRODUCTION Electromagnetic induction (EMI) sensors have been studied extensively for their use in detecting buried land-mines.
['Matthew Cook', 'Alina Zare', 'Dominic K. C. Ho']
Buried object detection using handheld WEMI with task-driven extended functions of multiple instances