Two-Stage Augmented Kernel Matrix for Object Recognition

  • Muhammad Awais
  • Fei Yan
  • Krystian Mikolajczyk
  • Josef Kittler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)


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.


Feature Space Object Recognition Mean Average Precision Feature Channel Multiple Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Muhammad Awais
    • 1
  • Fei Yan
    • 1
  • Krystian Mikolajczyk
    • 1
  • Josef Kittler
    • 1
  1. 1.Centre for Vision, Speech and Signal Processing (CVSSP)University of SurreyUK

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