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Two-stage augmented kernel matrix for object recognition

Awais, M, Yan, F, Mikolajczyk, K and Kittler, J (2011) Two-stage augmented kernel matrix for object recognition In: MCS 2011: 10th International Workshop on Multiple Classifier Systems, 2011-06-15 - 2011-06-17, Naples, Italy.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Awais, M
Yan, F
Mikolajczyk, K
Kittler, J
Date : 2011
DOI : 10.1007/978-3-642-21557-5_16
Contributors :
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 15 Oct 2014 16:54
Last Modified : 31 Oct 2017 16:59

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