In this paper, we propose a set of new image features for the classification of zoom-endoscopy images. The feature extraction step is based on fitting a two-parameter Weibull distribution to the wavelet coefficient magnitudes of subbands obtained from a complex wavelet transform variant. We show, that the shape and scale parameter possess more discriminative power than the classic mean and standard deviation based features for complex subband coefficient magnitudes. Furthermore, we discuss why the commonly used Rayleigh distribution model is suboptimal in our case.
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