On using Feature Descriptors as Visual Words for Object Detection within X-ray Baggage Security Screening

Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within baggage security X-ray imagery. Using a classical BoVW model with a range of feature point detectors a...

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Bibliographic Details
Published in:	7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016) p. 12
Main Authors:	Kundegorski, M.E, Akcay, S, Devereux, M, Mouton, A, Breckon, T.P
Format:	Conference Proceeding
Language:	English
Published:	Stevenage, UK IET 2016 The Institution of Engineering & Technology
Subjects:	Airline security Baggage Classification Combinatorial mathematics Computational complexity Computer vision and image processing techniques Image classification Image recognition Knowledge engineering techniques Object recognition Passenger screening Screening Security Small arms Support vector machines X ray imagery X-ray and gamma-ray equipment BoVW representation X-ray baggage security screening optimal statistical accuracy support vector machines image classification threat detection object detection visual words feature point detectors SVM FAST-SURF feature detector vocabulary size X-ray imaging image complexity bag-of-visual-words representation support vector machine X-ray imagery feature extraction Random Forest classification decision trees image representation feature point descriptors firearms detection computational complexity
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Summary:	Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within baggage security X-ray imagery. Using a classical BoVW model with a range of feature point detectors and descriptors, supported by both Support Vector Machine (SVM) and Random Forest classification, we illustrate the current performance capability of approaches following this image classification paradigm over a large X-ray baggage imagery data set. An optimal statistical accuracy of 0.94 (true positive: 83%; false positive: 3.3%) is achieved using a FAST-SURF feature detector and descriptor combination for a firearms detection task. Our results indicate comparative levels of performance for BoVW based approaches for this task over extensive variations in feature detector, feature descriptor, vocabulary size and final classification approach. We further demonstrate a by-product of such approaches in using feature point density as a simple measure of image complexity available as an integral part of the overall classification pipeline. The performance achieved characterises the potential for BoVW based approaches for threat object detection within the future automation of X-ray security screening against other contemporary approaches in the field.
ISBN:	1785614002 9781785614002
DOI:	10.1049/ic.2016.0080