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
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.
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