JACIII Vol.13 No.4 pp. 400-406
doi: 10.20965/jaciii.2009.p0400


Averaging Forest for Online Vision

Hassab Elgawi Osman

Imaging Science and Engineering Lab, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan

November 24, 2008
March 10, 2009
July 20, 2009
random forest (RF), object recognition, histogram, covariance descriptor.

In this study we consider vision as a binary classification problem, where an ensemble of decision-tree-based classifiers is trained on-line, new images are continuously added and the recognition decision is made without delay. Ensemble of decision trees is combined into a forest classifier using averaging, generate an on-line Random Forest (RF) classifier. First we employ object descriptor model based on a bag of covariance matrices, to represent an object features, then run our on-line RF learner to select object descriptors and to learn object classifiers. Validation of our proposal with empirical studies in the GRAZ02 dataset domain demonstrates its superior performance over histogram-based counterparts, yielding object recognition performance comparable to state-of-the-art standard RF, AdaBoost, and SVM classifiers, even when only 10% of the training examples are used.

Cite this article as:
Hassab Elgawi Osman, “Averaging Forest for Online Vision,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.4, pp. 400-406, 2009.
Data files:
  1. [1] M.J. Swain and D.H. Ballard, “Color indexing,” IJCV, 7(1), pp. 11-32, 1999.
  2. [2] B. Schiele and J.L. Crowley, “Recognition without correspondence using multidimensional receptive field histograms,” IJCV, 36(1), pp. 31-50, 2000.
  3. [3] T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” IJCV, 43(1), pp. 29-44, 2000.
  4. [4] H. Schneiderman and T. Kanade, “A statistical method for 3D object detection applied to faces and cars,” In Proc. CVPR, Vol.I, pp. 746-751, 2000.
  5. [5] S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts,” IEEE-PAMI, 24(4), pp. 509-522, 2004.
  6. [6] D. Lowe, “Distinctive image features from scale-invariant keypoints,” IJCV, 60(2), pp. 91-110, 2004.
  7. [7] O. Tuzel, F. Porikli, and P. Meer, “Region covariance: A fast descriptor for detection and classification,” In Proc. ECCV, pp. 589-600, 2006.
  8. [8] L. Fei-Fei, R. Fergus and P. Perona, “A Bayesian approach to unsupervised learning of object categories,” Proc. ICCV, pp. 1134-1141, 2003.
  9. [9] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” Proc. CVPR, Vol.1, pp. 511-518, 2001.
  10. [10] L. Breiman, “Random Forests,” Machine Learning, 45(1), pp. 5-32, 2001.
  11. [11] L. Breiman, “Bagging predictors,” Machine Learning, 24(2), pp. 123-140, 1996.
  12. [12] H. Elgawi Osman, “Online Random Forests based on CorrFS and CorrBE,” In Proc. IEEE workshop on online classification, CVPR, pp. 1-7, 2008.
  13. [13] F. Moomsmann, B. Triggs, and F. Jurie, “Fast discriminative visual codebooks using randomized clustering forests,” NIPS 19, pp. 985-992, 2006.
  14. [14] J. Winn and A. Criminisi, “Object class recognition at a glance,” CVPR, Video demo, 2006.
  15. [15] H. Elgawi Osman, “A binary Classification and Online Vision,” In Proc. Int. Joint Conf. on Neural Networks, IJCNN, 2009.
  16. [16] A.Bosch, A.Zisserman, and X.Munoz, “Image Classification Using Random Forests and Ferns,” ICCV, pp. 1-8, 2007.
  17. [17] J. Shotton, M. Johnson, and R. Cipolla, “Semantic Texton Forests for Image Categorization and Segmentation,” CVPR, pp. 1-8, 2008.
  18. [18] F. Schroff, A. Criminisi, and A. Zisserman, “Object Class Segmentation using Random Forests,” BMVC 2008.
  19. [19] Y. Amit and D. Geman, “Shape quantization and recognition with randomized trees,” Neural Computation, 9(7), pp. 1545-1588, 1997.
  20. [20] V. Lepetit, P. Lagger, and P. Fua, “Randomized trees for real-time keypoint recognition,” In Proc. CVPR, 2(2), pp. 775-781, 2005.
  21. [21] L. Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone, “Classification and regression trees,” Wadsworth Inc., Belmont, California, 1984.
  22. [22] K.-P. Karman and A. von Brandt, “Moving object recognition using an adaptive background memory in Time-varying Image Processing and Moving Object Recognition,” Capellini, Ed., Vol.II. Amsterdam, The Netherlands: Elsevier, pp. 297-307, 1990.
  23. [23] A. Opelt, M. Fussenegger, A. Pinz, and P. Auer, “Generic object recognition with boosting,” IEEE TPAMI, 28(3), pp. 416-431, 2006.
  24. [24] A. Opelt and A. Pinz, “Object Localization with boosting and weak supervision for generic object recognition,” In Kalvianen H. et al. (Eds.), SCIA 2005, LNCS 3450, pp. 862-871, 2005.
  25. [25] A. Opelt, M. Fussenegger, A. Pinz, and P. Auer, “Weak hypotheses and boosting for generic object detection and recognition,” In Proc. ECCV, Vol.2, pp. 71-84, 2004.
  26. [26] J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. “Local features and kernels for classifcation of texture and object categories: An in-depth study,” Technical Report RR-5737, INRIA Rhén-Alpes, 2005.

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

Last updated on Mar. 05, 2021