Imperial College London

DrKrystianMikolajczyk

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 6220k.mikolajczyk

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Awais:2011:10.1007/978-3-642-23780-5_19,
author = {Awais, M and Yan, F and Mikolajczyk, K and Kittler, J},
doi = {10.1007/978-3-642-23780-5_19},
pages = {140--155},
publisher = {Springer},
title = {Novel fusion methods for pattern recognition},
url = {http://dx.doi.org/10.1007/978-3-642-23780-5_19},
year = {2011}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Over the last few years, several approaches have been proposed for information fusion including different variants of classifier level fusion (ensemble methods), stacking and multiple kernel learning (MKL). MKL has become a preferred choice for information fusion in object recognition. However, in the case of highly discriminative and complementary feature channels, it does not significantly improve upon its trivial baseline which averages the kernels. Alternative ways are stacking and classifier level fusion (CLF) which rely on a two phase approach. There is a significant amount of work on linear programming formulations of ensemble methods particularly in the case of binary classification. In this paper we propose a multiclass extension of binary ν-LPBoost, which learns the contribution of each class in each feature channel. The existing approaches of classifier fusion promote sparse features combinations, due to regularization based on 1-norm, and lead to a selection of a subset of feature channels, which is not good in the case of informative channels. Therefore, we generalize existing classifier fusion formulations to arbitrary p -norm for binary and multiclass problems which results in more effective use of complementary information. We also extended stacking for both binary and multiclass datasets. We present an extensive evaluation of the fusion methods on four datasets involving kernels that are all informative and achieve state-of-the-art results on all of them.
AU - Awais,M
AU - Yan,F
AU - Mikolajczyk,K
AU - Kittler,J
DO - 10.1007/978-3-642-23780-5_19
EP - 155
PB - Springer
PY - 2011///
SN - 0302-9743
SP - 140
TI - Novel fusion methods for pattern recognition
UR - http://dx.doi.org/10.1007/978-3-642-23780-5_19
ER -