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

@article{Mikolajczyk:2006:10.1109/CVPR.2006.202,
author = {Mikolajczyk, K and Leibe, B and Schiele, B},
doi = {10.1109/CVPR.2006.202},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {26--33},
title = {Multiple object class detection with a generative model},
url = {http://dx.doi.org/10.1109/CVPR.2006.202},
volume = {1},
year = {2006}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation allows to represent individual images as well as various objects classes in a single, scale and rotation invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. The approach is highly efficient due to fast clustering and matching methods capable of dealing with millions of high dimensional features. The system shows excellent performance on several object categories over a wide range of scales, in-plane rotations, background clutter, and partial occlusions. The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem. © 2006 IEEE.
AU - Mikolajczyk,K
AU - Leibe,B
AU - Schiele,B
DO - 10.1109/CVPR.2006.202
EP - 33
PY - 2006///
SN - 1063-6919
SP - 26
TI - Multiple object class detection with a generative model
T2 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
UR - http://dx.doi.org/10.1109/CVPR.2006.202
VL - 1
ER -