In this paper, two methods for discriminative multiple instance target characterization, MI-
SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target …

Abstract The Multiple Instance Hybrid Estimator for discriminative target characterization
from imprecisely labeled hyperspectral data is presented. In many hyperspectral target …

Because of appearance variations, training samples of the tracked targets collected by the
online tracker are required for updating the tracking model. However, this often leads to …

In this paper, multiple instance learning (MIL) algorithms to automatically perform root
detection and segmentation in minirhizotron imagery using only image-level labels are …

We present a multi-class multiple instance learning (MIL) algorithm using the dictionary
learning framework where the data is given in the form of bags. Each bag contains multiple …

Multi-instance learning (MIL) is a generalized form of supervised learning that attempts to
extract useful information from sets of instances, known as bags. In practice, besides positive …

Multiple-instance learning (MIL) has been a popular topic in the study of pattern recognition
for years due to its usefulness for such tasks as drug activity prediction and image/text …

Abstract The Multiple Instance Learning (MIL) framework has been extensively used to solve
weakly labeled visual classification problems, where each image or video is treated as a …

In many remote sensing and hyperspectral image analysis applications, precise ground truth
information is unavailable or impossible to obtain. Imprecision in ground truth often results …

In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE,
are presented. MI-SMF and MI-ACE estimate a discriminative target signature from …