Averaging Forest for Online Vision

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 13; no. 4; pp. 400 - 406
• Main Author: Osman, Hassab Elgawi
• Format: Journal Article
• Language: English
• Published: 20.07.2009
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2009.p0400

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.