Improved Object Recognition with Decision Trees Using Subspace Clustering
Billy Peralta and Luis Alberto Caro
Catholic University of Temuco
Rudecindo Ortega 02950, Temuco, Chile
-  G. Littlewort, M. S. Bartlett, I. Fasel, J. Chenu, T. Kanda, H. Ishiguro, and J. R. Movellan, “Towards social robots: Automatic evaluation of human-robot interaction by face detection and expression classification,” Advances in Neural Information Processing Systems, Vol.16, pp. 1563-1570, MIT Press, 2003.
-  S. Ekvall, D. Kragic, and P. Jensfelt, “Object detection and mapping for service robot tasks,” Robotica, Vol.25, No.2, pp. 175-187, 2007.
-  K. Huebner, M. Björkman, B. Rasolzadeh, M. Schmidt, and D. Kragic, “Integration of visual and shape attributes for object action complexes,” ICVS’08: Proc. of the 6th Int. Conf. on Computer Vision Systems, pp. 13-22, Berlin, Heidelberg, Springer-Verlag, 2008.
-  D. Lowe, “Object Recognition from Local Scale-Invariant Features,” pp. 1150-1157, 1999.
-  P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” pp. 511-518, 2001.
-  R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale-invariant learning,” CVPR, pp. 264-271, 2003.
-  L. Fei-Fei, “A bayesian hierarchical model for learning natural scene categories,” CVPR, Vol.2, pp. 524-531, 2005.
-  P. F. Felzenszwalb, D. A. McAllester, and D. Ramanan, “A discriminatively trained, multiscale, deformable part model,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
-  A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
-  R. Mare, P. Geurts, J. Piater, and L. Wehenkel, “Decision trees and random subwindows for object recognition,” Int. Conf. on Machine Learning: Workshop on Machine Learning Techniques for Processing Multimedia Content, 2005.
-  L. Breiman, J. H. Friedman, R. Olshen, and C. J. Stone, “Classification and Regression Trees,” Wadsworth, Belmont, California, 1984.
-  L. Breiman, “Bagging Predictors,” Machine Learning, Vol.26, pp. 123-140, 1996.
-  R. E. Schapire. “A Brief Introduction to Boosting,” 1999.
-  L. B. Statistics and L. Breiman, “Random Forests,” Machine Learning, pp. 5-32, 2001.
-  P. Geurts, D. Ernst, and L. Wehenkel. “Extremely Randomized Trees,” 2006.
-  T. Deselaers, D. Keysers, and H. Ney, “Discriminative Training for Object Recognition Using Image Patches,” CVPR’05: Proc. of the 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’05) – Vol.2, pp. 157-162, Washington DC, USA, IEEE Computer Society, 2005.
-  M. Vidal-Naquet and S. Ullman, “Object Recognition with Informative Features and Linear Classification,” ICCV’03: Proc. of the 9th IEEE Int. Conf. on Computer Vision, pp. 281, Washington DC, USA, IEEE Computer Society, 2003.
-  P. Blackwell and D. Austin, “Appearance Based Object Recognition with a Large Dataset using Decision Trees,” 2004.
-  D. Nister and H. Stewenius, “Scalable Recognition with a Vocabulary Tree,” CVPR’06: Proc. of the 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 2161-2168, Washington DC, USA, IEEE Computer Society, 2006.
-  J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide-baseline stereo from maximally stable extremal regions,” Image and Vision Computing, Vol.22, No.10, pp. 761-767, 2004.
-  M. Shahbaz, A. Guergachi, A. Noreen, and M. Shaheen, “Classification by Object Recognition in Satellite Images by using Data Mining,” Proc. of the World Congress on Engineering, pp. 406-413, 2012.
-  W. Damak, I. Rebai, and I. Kallel, “Semantic object recognition by merging decision tree with object ontology,” Int. Conf. on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 65-70, March 2014.
-  M. J. Choi, A. Torralba, and A. S. Willsky, “Context models and out-of-context objects,” Pattern Recognition Letters, Vol.33, No.7, pp. 853-862, 2012.
-  F. Moosmann, D. Larlus, and F. Jurie, “Learning saliency maps for object categorization,” European Conf. in Computer Vision: Int. Workshop on the Representation and Use of Prior Knowledge in Vision, Springer, 2006.
-  N. Dalal, B. Triggs, and C. Schmid, “Human detection using oriented histograms of flow and appearance,” European Conf. in Computer Vision, pp. 428-441, Springer, 2006.
-  H. Bay, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features,” European Conf. in Computer Vision, pp. 404-417, 2006.
-  F. Perronnin, J. Sánchez, and T. Mensink, “Improving the fisher kernel for large-scale image classification,” European Conf. in Computer Vision, pp. 143-156, Springer, 2010.
-  X. Zhou, K. Yu, T. Zhang, and T. S. Huang, “Image classification using super-vector coding of local image descriptors,” European Conf. in Computer Vision, pp. 141-154, Springer, 2010.
-  R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, “Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications,” ACM SIGMOD Int. Conf. on Management of Data, pp. 94-105, 1998.
-  A. Rabinovich, A. Vedaldi, C. Galleguillos, E.Wiewiora, and S. Belongie, “Objects in context,” Int. Conf. on Computer Vision, pp. 1-8, IEEE, 2007.
-  C. Galleguillos, A. Rabinovich, and S. Belongie, “Object categorization using co-occurrence, location and appearance,” Conf. on Computer Vision and Pattern Recognition, pp. 1-8, June 2008.
-  D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE Trans. on Systems, Man and Cybernetics, Vol.21, No.3, pp. 660-674, May 1991.
-  N. X. Vinh, J. Epps, and J. Bailey, “Information theoretic measures for clusterings comparison: is a correction for chance necessary?,” Int. Conf. on Machine Learning, pp. 1073-1080, New York, NY, USA, ACM, 2009.
-  F. Fleuret, “Fast Binary Feature Selection with Conditional Mutual Information,” J. Mach. Learn. Res., Vol.5, pp. 1531-1555, 2004.
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