Establishing cross-spectral feature point correspondences is a challenging problem due to high textural and intensity changes between multisensor images. To overcome such changes, a new feature matching strategy is proposed. The proposed strategy is based on Gaussian mixture model-universal background model (GMM-UBM). GMM-UBM is widely used as a classifier in speech-related applications. GMM-UBM is used to establish feature point correspondences between multisensor images. Experiments were performed using 17 different state-of-the-art feature points and GMM-UBM was validated on two different image datasets. The experimental results show that the proposed GMM-UBM method outperforms traditional Euclidean distance-based feature matching strategies and provides better results on multisensor images.
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