3D object classification in baggage computed tomography imagery using randomised clustering forests

We investigate the feasibility of a codebook approach for the automated classification of threats in pre-segmented 3D baggage Computed Tomography (CT) security imagery. We compare the performance of five codebook models, using various combinations of sampling strategies, feature encoding techniques...

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Bibliographic Details
Main Authors: Mouton, Andre, Breckon, Toby P., Flitton, Greg T., Megherbi, Najla
Format: Conference Proceeding
Language:English
Subjects:
Bag-of-Words
baggage CT
Classification
Computed tomography
Computer vision
Conferences
Random forests
Support vector machines
Three-dimensional displays
Vegetation
Visualization
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Description
Summary:We investigate the feasibility of a codebook approach for the automated classification of threats in pre-segmented 3D baggage Computed Tomography (CT) security imagery. We compare the performance of five codebook models, using various combinations of sampling strategies, feature encoding techniques and classifiers, to the current state-of-the-art 3D visual cortex approach. We demonstrate an improvement over the state-of-the-art both in terms of accuracy as well as processing time using a codebook constructed via randomised clustering forests, a dense feature sampling strategy and an SVM classifier. Correct classification rates in excess of 98% and false positive rates of less than 1%, in conjunction with a reduction of several orders of magnitude in processing time, make the proposed approach an attractive option for the automated classification of threats in security screening settings.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2014.7026053