Top > JACIII > Improved Object Recognition with Decision Trees Using Subspace ...

single-jc.php

JACIII Vol.20 No.1 pp. 41-48
doi: 10.20965/jaciii.2016.p0041
(2016)

Paper:

Views over last 60 days: 875

Improved Object Recognition with Decision Trees Using Subspace Clustering

Billy Peralta and Luis Alberto Caro

Catholic University of Temuco
Rudecindo Ortega 02950, Temuco, Chile

Received:
July 23, 2015
Accepted:
October 23, 2015
Online released:
January 19, 2016
Published:
January 20, 2016
Creative Commons License
Keywords:
decision trees, object recognition, subspace clustering, random forest
Abstract
Generic object recognition algorithms usually require complex classificationmodels because of intrinsic difficulties arising from problems such as changes in pose, lighting conditions, or partial occlusions. Decision trees present an inexpensive alternative for classification tasks and offer the advantage of being simple to understand. On the other hand, a common scheme for object recognition is given by the appearances of visual words, also known as the bag-of-words method. Although multiple co-occurrences of visual words are more informative regarding visual classes, a comprehensive evaluation of such combinations is unfeasible because it would result in a combinatorial explosion. In this paper, we propose to obtain the multiple co-occurrences of visual words using a variant of the CLIQUE subspace-clustering algorithm for improving the object recognition performance of simple decision trees. Experiments on standard object datasets show that our method improves the accuracy of the classification of generic objects in comparison to traditional decision tree techniques that are similar, in terms of accuracy, to ensemble techniques. In future we plan to evaluate other variants of decision trees, and apply other subspace-clustering algorithms.
Cite this article as:
B. Peralta and L. Caro, “Improved Object Recognition with Decision Trees Using Subspace Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.1, pp. 41-48, 2016.
Data files:
References
  1. [1] 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.
  2. [2] S. Ekvall, D. Kragic, and P. Jensfelt, “Object detection and mapping for service robot tasks,” Robotica, Vol.25, No.2, pp. 175-187, 2007.
  3. [3] 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.
  4. [4] D. Lowe, “Object Recognition from Local Scale-Invariant Features,” pp. 1150-1157, 1999.
  5. [5] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” pp. 511-518, 2001.
  6. [6] R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale-invariant learning,” CVPR, pp. 264-271, 2003.
  7. [7] L. Fei-Fei, “A bayesian hierarchical model for learning natural scene categories,” CVPR, Vol.2, pp. 524-531, 2005.
  8. [8] 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.
  9. [9] 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.
  10. [10] 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.
  11. [11] L. Breiman, J. H. Friedman, R. Olshen, and C. J. Stone, “Classification and Regression Trees,” Wadsworth, Belmont, California, 1984.
  12. [12] L. Breiman, “Bagging Predictors,” Machine Learning, Vol.26, pp. 123-140, 1996.
  13. [13] R. E. Schapire. “A Brief Introduction to Boosting,” 1999.
  14. [14] L. B. Statistics and L. Breiman, “Random Forests,” Machine Learning, pp. 5-32, 2001.
  15. [15] P. Geurts, D. Ernst, and L. Wehenkel. “Extremely Randomized Trees,” 2006.
  16. [16] 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.
  17. [17] 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.
  18. [18] P. Blackwell and D. Austin, “Appearance Based Object Recognition with a Large Dataset using Decision Trees,” 2004.
  19. [19] 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.
  20. [20] 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.
  21. [21] 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.
  22. [22] 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.
  23. [23] 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.
  24. [24] 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.
  25. [25] 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.
  26. [26] H. Bay, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features,” European Conf. in Computer Vision, pp. 404-417, 2006.
  27. [27] 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.
  28. [28] 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.
  29. [29] 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.
  30. [30] A. Rabinovich, A. Vedaldi, C. Galleguillos, E.Wiewiora, and S. Belongie, “Objects in context,” Int. Conf. on Computer Vision, pp. 1-8, IEEE, 2007.
  31. [31] 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.
  32. [32] 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.
  33. [33] 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.
  34. [34] F. Fleuret, “Fast Binary Feature Selection with Conditional Mutual Information,” J. Mach. Learn. Res., Vol.5, pp. 1531-1555, 2004.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024