This paper proposes a fast approach to spectral image segmentation. In the algorithm, two popular techniques are
extended and applied to spectral color images: the mean-shift filtering and the kernel-based clustering. We claim that
segmentation should be completed under illuminant F11 rather than directly using the original spectral reflectance,
because such illumination can reduce data variability and expedite the following filtering. The modes obtained in the
mean-shift filtering represent the local features of spectral images, and will be applied to segmentation in place of pixels.
Since the modes are generally small in number, the eigendecomposition of kernel matrices, the crucial step in the kernelbased
clustering, becomes much easier. The combination of these two techniques can efficiently enhance the
performance of segmentation. Experiments show that the proposed segmentation method is feasible and very promising
for spectral color images.
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