Machine Learning
Shape quantization and recognition with randomized trees
Neural Computation
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Machine Learning
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object localization with boosting and weak supervision for generic object recognition
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Ensemble for high recognition performance FPGA
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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. The ensemble of Decision Tree classifier is combined into aforest classifier using averaging, generate On-line Random Forest (RF) classifier. First we employ object descriptor model based on bag of covariance matrices, to represent an object visual features then run our online RF learner to select object descriptors and to learn an object classifier. Validation of the method with empirical studies in the domain of the GRAZ02 dataset shows its superior performance over those of histograms based, subsequently yields in object recognition performance comparable to the state-of-art standard RF, AdaBoost, and SVM classifiers, even when only 10% training examples are used.