Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Computer Vision



+44 (0)20 7594 6220k.mikolajczyk




Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Awais, M and Yan, F and Mikolajczyk, K and Kittler, J},
doi = {10.1007/978-3-642-21557-5_16},
pages = {137--146},
publisher = {Springer},
title = {Two-stage augmented kernel matrix for object recognition},
url = {},
year = {2011}

RIS format (EndNote, RefMan)

AB - Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recognition problem. Aim of MKL is to learn optimal combination of kernels formed from different features, thus, to learn importance of different feature spaces for classification. Augmented Kernel Matrix (AKM) has recently been proposed to accommodate for the fact that a single training example may have different importance in different feature spaces, in contrast to MKL that assigns same weight to all examples in one feature space. However, AKM approach is limited to small datasets due to its memory requirements. We propose a novel two stage technique to make AKM applicable to large data problems. In first stage various kernels are combined into different groups automatically using kernel alignment. Next, most influential training examples are identified within each group and used to construct an AKM of significantly reduced size. This reduced size AKM leads to same results as the original AKM. We demonstrate that proposed two stage approach is memory efficient and leads to better performance than original AKM and is robust to noise. Results are compared with other state-of-the art MKL techniques, and show improvement on challenging object recognition benchmarks.
AU - Awais,M
AU - Yan,F
AU - Mikolajczyk,K
AU - Kittler,J
DO - 10.1007/978-3-642-21557-5_16
EP - 146
PB - Springer
PY - 2011///
SN - 0302-9743
SP - 137
TI - Two-stage augmented kernel matrix for object recognition
UR -
ER -