Cite

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

S. Gould, J. Zhao, X. He, and Y. Zhang, “Superpixel graph label transfer with learned distance metric,” in ECCV, 2014.10.1007/978-3-319-10590-1_41 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

J. H. Friedman, “Greedy function approximation: A gradient boosting machine,”Annals of Statistics, vol. 29, pp. 1189–1232, 2000. Search in Google Scholar

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 Search in Google Scholar

J. H. Friedman, “Stochastic gradient boosting,”Comput. Stat. Data Anal., vol. 38, no. 4, pp. 367– 378, Feb. 2002.10.1016/S0167-9473(01)00065-2 Search in Google Scholar

A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,”Frontiers in Neurorobotics, vol. 7, 2013.10.3389/fnbot.2013.00021388582624409142 Search in Google Scholar

B. Andres, B. T., and J. H. Kappes, “OpenGM: A C++ library for discrete graphical models,”ArXiv e-prints, 2012. Search in Google Scholar

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.Search in Google Scholar

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