INCREMENTAL MACHINE LEARNING APPROACH FOR COMPONENT-BASED RECOGNITION

Osman Hassab Elgawi

Abstract

This study proposes an on-line machine learning approach for object recognition, where new images are continuously added and the recognition decision is made without delay. Random forest (RF) classifier has been extensively used as a generative model for classification and regression applications. We extend this technique for the task of building incremental component-based detector. First we employ object descriptor model based on bag of covariance matrices, to represent an object region then run our on-line RF learner to select object descriptors and to learn an object classifier. Experiments of the object recognition are provided to verify the effectiveness of the proposed approach. Results demonstrate that the propose model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers.

References

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Paper Citation


in Harvard Style

Hassab Elgawi O. (2009). INCREMENTAL MACHINE LEARNING APPROACH FOR COMPONENT-BASED RECOGNITION . In Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009) ISBN 978-989-8111-68-5, pages 5-12. DOI: 10.5220/0001783100050012


in Bibtex Style

@conference{imagapp09,
author={Osman Hassab Elgawi},
title={INCREMENTAL MACHINE LEARNING APPROACH FOR COMPONENT-BASED RECOGNITION},
booktitle={Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)},
year={2009},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001783100050012},
isbn={978-989-8111-68-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)
TI - INCREMENTAL MACHINE LEARNING APPROACH FOR COMPONENT-BASED RECOGNITION
SN - 978-989-8111-68-5
AU - Hassab Elgawi O.
PY - 2009
SP - 5
EP - 12
DO - 10.5220/0001783100050012