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

Mouton, A. and Breckon, T.P. and Flitton, G.T. and Megherbi, N. (2014) '3D object classification in baggage computed tomography imagery using randomised clustering forests.', in Image Processing (ICIP), 2014 IEEE International Conference on, 27-30 October 2014, Paris, France ; proceedings. , pp. 5202-5206.


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 [1]. 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 [2], 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.

Item Type:Book chapter
Keywords:Computed tomography, Computer vision, Conferences, Support vector machines, Three-dimensional displays, Vegetation, Visualization, Bag-of-Words, Classification, Random forests, baggage CT.
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:03 February 2015
Date of first online publication:October 2014
Date first made open access:No date available

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