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

Mouton, A.; Breckon, T.P.; Flitton, G.T.; Megherbi, N.

Abstract

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.

Citation

Mouton, A., Breckon, T., Flitton, G., & 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 (5202-5206). https://doi.org/10.1109/icip.2014.7026053

• Conference Name: Proc. International Conference on Image Processing
• Publication Date: Oct 30, 2014
• Deposit Date: Dec 9, 2014
• Publicly Available Date: Feb 3, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 5202-5206
• Book Title: Image Processing (ICIP), 2014 IEEE International Conference on, 27-30 October 2014, Paris, France ; proceedings.
• DOI: https://doi.org/10.1109/icip.2014.7026053
• Keywords: Computed tomography, Computer vision, Conferences, Support vector machines, Three-dimensional displays, Vegetation, Visualization, Bag-of-Words, Classification, Random forests, baggage CT.
• Related Public URLs: http://www.durham.ac.uk/toby.breckon/publications/papers/mouton14randomised.pdf