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Proceedings Paper

An incremental knowledge assimilation system (IKAS) for mine detection
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Paper Abstract

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

Paper Details

Date Published: 20 April 2010
PDF: 10 pages
Proc. SPIE 7678, Ocean Sensing and Monitoring II, 76780P (20 April 2010); doi: 10.1117/12.853022
Show Author Affiliations
Jake Porway, UtopiaCompression Corp. (United States)
Chaitanya Raju, UtopiaCompression Corp. (United States)
Karthik Mahesh Varadarajan, UtopiaCompression Corp. (United States)
Hieu Nguyen, UtopiaCompression Corp. (United States)
Joseph Yadegar, UtopiaCompression Corp. (United States)

Published in SPIE Proceedings Vol. 7678:
Ocean Sensing and Monitoring II
Weilin (Will) Hou; Robert A. Arnone, Editor(s)

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