A multi-instance learning approach to filtering images for presentation to analysts

Abstract

Listen

Translate article

This paper proposes an image filtering and retrieval system driven by the multi-instance learning (MIL) algorithm. This system is aimed at improving the mission effectiveness of human analysts in searching through imagery for environmental, defense, or other purposes. Thus, the system is tuned and the experimental results are measured in terms of the true positive rate in predicted labels. While MIL has been used in image retrieval before, this paper examines how different tasks and feature spaces impact the performance of the algorithm. Images are translated into the single blob with neighbors (SBN) feature space, a novel feature space called color, texture, and shape (CTS), and a combined SBN and CTS feature space, for processing by the MIL algorithm. The paper introduces a feature space selection step in the classification process and shows that the true positive rate can be increased through the addition of this step.