Content based image retrieval for matching images of improvised explosive devices in which snake initialization is viewed as an inverse problem

Scott T. Acton; Andrew D. Gilliam; Bing Li; Adam Rossi

doi:10.1117/12.775117

26 February 2008 Content based image retrieval for matching images of improvised explosive devices in which snake initialization is viewed as an inverse problem

Scott T. Acton, Andrew D. Gilliam, Bing Li, Adam Rossi

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Proceedings Volume 6814, Computational Imaging VI; 68140A (2008) https://doi.org/10.1117/12.775117
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Improvised explosive devices (IEDs) are common and lethal instruments of terrorism, and linking a terrorist entity to a specific device remains a difficult task. In the effort to identify persons associated with a given IED, we have implemented a specialized content based image retrieval system to search and classify IED imagery. The system makes two contributions to the art. First, we introduce a shape-based matching technique exploiting shape, color, and texture (wavelet) information, based on novel vector field convolution active contours and a novel active contour initialization method which treats coarse segmentation as an inverse problem. Second, we introduce a unique graph theoretic approach to match annotated printed circuit board images for which no schematic or connectivity information is available. The shape-based image retrieval method, in conjunction with the graph theoretic tool, provides an efficacious system for matching IED images. For circuit imagery, the basic retrieval mechanism has a precision of 82.1% and the graph based method has a precision of 98.1%. As of the fall of 2007, the working system has processed over 400,000 case images.

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Scott T. Acton, Andrew D. Gilliam, Bing Li, and Adam Rossi "Content based image retrieval for matching images of improvised explosive devices in which snake initialization is viewed as an inverse problem", Proc. SPIE 6814, Computational Imaging VI, 68140A (26 February 2008); https://doi.org/10.1117/12.775117

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KEYWORDS

Image segmentation

Improvised explosive devices

Image retrieval

Inverse problems

Wavelets

Content based image retrieval

Convolution

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