J. Shotton, J. Winn, C. Rother, and A. Criminisi, “Textonboost for image understanding: Multiclass object recognition and segmentation by jointly modeling texture, layout, and context,”Int. J. Comput. Vision, vol. 81, no. 1, pp. 2–23, Jan. 2009.10.1007/s11263-007-0109-1
L. Ladicky, C. Russell, P. Kohli, and P. H. S. Torr, “Associative hierarchical crfs for object class image segmentation,” in Computer Vision, 2009 IEEE 12th International Conference on, Sept 2009, pp. 739–746.10.1109/ICCV.2009.5459248
P. KrähenbÜhl and V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” in Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, Eds. Curran Associates, Inc., 2011, pp. 109–117.
X. Boix, J. M. Gonfaus, J. van de Weijer, A. D. Bagdanov, J. S. Gual, and J. Gonzalez, “Harmony potentials - fusing global and local scale for semantic image segmentation.”International Journal of Computer Vision, vol. 96, no. 1, pp. 83–102, 2012.10.1007/s11263-011-0449-8
J. Alvarez, M. Salzmann, and N. Barnes, “Large-scale semantic co-labeling of image sets,” in Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, March 2014, pp. 501–508.10.1109/WACV.2014.6836060
J. Z. Ning Zhang, “A study of x-ray machine image local semantic features extraction model based on bag-ofwords for airport security,”Internatioanal Journal on Smart Sensing and Intelligent Systems, vol. 8, no. 1, p. 45, 2015.10.21307/ijssis-2017-748
Aprinaldi, I. Habibie, R. Rahmatullah, A. Kurniawan, A. Bowolaksono, W. Jatmiko, and B. Wi- weko, “Arcpso: Ellipse detection method using particle swarm optimization and arc combination,” in Advanced Computer Science and Information Systems (ICACSIS), ser. ICACSIS 2014. IEEE, 2014, pp. 408– 413.10.1109/ICACSIS.2014.7065877
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,”http://www.pascal- network.org/challenges/VOC/voc2012/workshop/index.html.
L. Ladicky, C. Russell, P. Kohli, and P. H. S. Torr, “Inference methods for crfs with co-occurrence statistics,”International Journal of Computer Vision, vol. 103, no. 2, pp. 213–225, 2013.10.1007/s11263-012-0583-y
A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie, “Objects in context,” in Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, Oct 2007, pp. 1–8.10.1109/ICCV.2007.4408986
S. Gould, R. Fulton, and D. Koller, “Decomposing a scene into geometric and semantically consistent regions,” in Computer Vision, 2009 IEEE 12th International Conference on, Sept 2009, pp. 1–8.10.1109/ICCV.2009.5459211
A. Gupta, A. A. Efros, and M. Hebert, “Blocks world revisited: Image understanding using qualitative geometry and mechanics,” in European Conference on Computer Vision(ECCV), 2010.10.1007/978-3-642-15561-1_35
A. Gupta and L. S. Davis, “Beyond nouns: Exploiting prepositions and comparative adjectives for learning visual classifiers,” in Proceedings of the 10th European Conference on Computer Vision: Part I, ser. ECCV ‘08. Berlin, Heidelberg: Springer-Verlag, 2008, pp. 16–29.
S. Gould, J. Rodgers, D. Cohen, G. Elidan, and D. Koller, “Multi-class segmentation with relative location prior.”International Journal of Computer Vision, vol. 80, no. 3, pp. 300–316, 2008.10.1007/s11263-008-0140-x
S. Divvala, D. Hoiem, J. Hays, A. Efros, and M. Hebert, “An empirical study of context in object detection,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, June 2009, pp. 1271–1278.10.1109/CVPR.2009.5206532
M. J. Choi, J. Lim, A. Torralba, and A. Willsky, “Exploiting hierarchical context on a large database of object categories,” in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, June 2010, pp. 129–136.10.1109/CVPR.2010.5540221
N. E. Maillot and M. Thonnat, “Ontology based complex object recognition,”Image and Vision Computing, vol. 26, no. 1, pp. 102 – 113, 2008, cognitive Vision-Special Issue.10.1016/j.imavis.2005.07.027
J. Tighe and S. Lazebnik, “Understanding scenes on many levels,” in Proceedings of the 2011 International Conference on Computer Vision, ser. ICCV ‘11. Washington, DC, USA: IEEE Computer Society, 2011, pp. 335–342.10.1109/ICCV.2011.6126260
A. Oliva and A. Torralba, “Modeling the shape ofthe scene: A holistic representation ofthe spatial envelope,”Int. J. Comput. Vision, vol. 42, no. 3, pp. 145–175, May 2001.10.1023/A:1011139631724
A. J. Smola and B. SchÖlkopf, “A tutorial on support vector regression,”Statistics and Computing, vol. 14, no. 3, pp. 199–222, Aug. 2004.10.1023/B:STCO.0000035301.49549.88
G. Li, H. Meng, M. Q. Yang, and J. Y. Yang, “Combining support vector regression with feature selection for multivariate calibration,”Neural Computing and Applications, vol. 18, no. 7, pp. 813–820, 2009.10.1007/s00521-008-0202-6
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Pretten- hofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,”Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.