Paper
20 April 2010 An incremental knowledge assimilation system (IKAS) for mine detection
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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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jake Porway, Chaitanya Raju, Karthik Mahesh Varadarajan, Hieu Nguyen, and Joseph Yadegar "An incremental knowledge assimilation system (IKAS) for mine detection", Proc. SPIE 7678, Ocean Sensing and Monitoring II, 76780P (20 April 2010); https://doi.org/10.1117/12.853022
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Cited by 1 scholarly publication.
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KEYWORDS
Target detection

Image filtering

Land mines

Mining

Classification systems

Image classification

Detection and tracking algorithms

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