Averaging Forest for Online Vision
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 av...
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| Vydáno v: | Journal of advanced computational intelligence and intelligent informatics Ročník 13; číslo 4; s. 400 - 406 |
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| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
20.07.2009
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| ISSN: | 1343-0130, 1883-8014 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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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| ISSN: | 1343-0130 1883-8014 |
| DOI: | 10.20965/jaciii.2009.p0400 |