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http://dx.doi.org/10.7471/ikeee.2019.23.4.1134

KNN-Based Automatic Cropping for Improved Threat Object Recognition in X-Ray Security Images  

Dumagpi, Joanna Kazzandra (Dept. of Electronics and Communications Engineering, Kwangwoon University)
Jung, Woo-Young (Dept. of Electronics and Communications Engineering, Kwangwoon University)
Jeong, Yong-Jin (Dept. of Electronics and Communications Engineering, Kwangwoon University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1134-1139 More about this Journal
Abstract
One of the most important applications of computer vision algorithms is the detection of threat objects in x-ray security images. However, in the practical setting, this task is complicated by two properties inherent to the dataset, namely, the problem of class imbalance and visual complexity. In our previous work, we resolved the class imbalance problem by using a GAN-based anomaly detection to balance out the bias induced by training a classification model on a non-practical dataset. In this paper, we propose a new method to alleviate the visual complexity problem by using a KNN-based automatic cropping algorithm to remove distracting and irrelevant information from the x-ray images. We use the cropped images as inputs to our current model. Empirical results show substantial improvement to our model, e.g. about 3% in the practical dataset, thus further outperforming previous approaches, which is very critical for security-based applications.
Keywords
KNN; automatic cropping; x-ray security images; threat object recognition; anomaly detection;
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1 D. Mery, E. Svec, M. Arias, V. Riffo, J. M. Saavedra and S. Banarjee, "Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection," IEEE Trans. Syst., Man., Cybern, Syst., vol.47, no.4, pp.682-692, 2017. DOI: 10.1109/TSMC.2016.2628381   DOI
2 S. Ackay, M. E. Kundegorski, C. G. Willcocks and T. P. Breckon, "Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-Ray Baggage Security Imagery," IEEE Trans. Inf. Forensics Security, vol.13, no.9, pp.2203-2215, 2018. DOI: 10.1109/TIFS.2018.2812196   DOI
3 D. Mery, V. Riffo and U. Zscherpel et al., "GDXray: The Database of X-ray Images for Nondestructive Testing," J Nondestruct Eval, 2015. DOI: 10.1007/s10921-015-0315-7
4 C. Miao, L. Xie, F. Wan, C. Su, H. Liu, J. Jiao and Q. Ye, "SIXray: A Large-scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit (CVPR), 2019.
5 H. He and E. A. Garcia, "Learning from Imbalanced Data," IEEE Trans. Knowl. Data Eng., vol.21, no.9, pp.1263-1284, 2009.   DOI
6 J. K. Dumagpi, W. Y. Jung and Y. J. Jeong, "A New GAN-based Anomaly Detection (GBAD) Approach for Mult7i-Threat Object Classification on Large-Scale X-Ray Security Images," IEICE Trans. Inf. & Syst., vol.E103-D, no.2,. 2020.
7 J. Chen, G. Bai, S. Liang, and Z. Li, "Automatic Image Cropping: A Computational Complexity Study," IEEE Conf. Comput. Vis. Pattern Recognit (CVPR), 2016. DOI: 10.1109/CVPR.2016.61
8 M. Nishiyama, T. Okabe, Y. Sato and I. Sato, "Sensation-based photo cropping," Proc. 17th ACM Int. Conf. Multimedia, 2009. DOI: 10.1145/1631272.1631384
9 A. Santella, M. Agrawala, D. DeCarlo, D. Salesin and M. Cohen, "Gaze-based interaction for semi-automatic photo cropping," Proc. SIGCHI Conf. Human Factors Computing Systems, 2006. DOI: 10.1145/1124772.1124886
10 M. Imura, Y. Tabata, R. Ishigaki, Y. Kuroda, Y. Uranishi and O. Oshiro, "Automatic Cropping Method for Chest Radiographs Based on Adaptive Binarization," Conf. Proc. IEEE Eng. Med. Biol. Soc., 2013. DOI: 10.1109/EMBC.2013.6611042.
11 K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016.
12 D. Mery, "Computer Vision for X-Ray Testing," Springer International Publishing, 2015. DOI: 10.1109/CVPR.2016.90
13 M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn and A. Zisserman, "The PASCAL Visual Object Classificaiton (VOC) Challenge," Int. J. Comput. Vis., vol.88, no.2, pp.303-338, 2010. DOI: 10.1007/s11263-009-0275-4   DOI
14 A. Paszke, S. Gross, S. Chintala, G. et al., "Automatic Differentiation in Pytorch," in NIPS Autodiff Workshop, 2017.
15 D. Mery, E. Svec and M. Arias, "Object Recognition in X-ray testing Using Adaptive Sparse Representations," Journal of Nondestructive Evaluation, Vol.35, No.45, pp.9, 2016. DOI: 10.1007/s10921-016-0362-8   DOI
16 V. Riffo and D. Mery, "Automated Detection of Threat Objects Using Adapted Implicit Shape Model," IEEE Trans. Syst., Man, Cybern. Syst., vol.46, no.4, 2016. DOI: 10.1109/TSMC.2015.2439233