Dynamic Fuzzy Clustering and Its Application in Motion Segmentation

Dynamic textures are common in natural scenes and have recently received great attention in video content analysis. A dynamic fuzzy clustering to automatically segment time-varying characteristics and phenomena is presented in this paper. First, compared with the existing models that assume a common prior distribution, which independently generates the labels, the prior distribution in our model is different for each observation and depends on the labels. In addition, in order to properly account for the neighboring observations during the learning step, we introduce the explicit assumptions of the hidden Markov random field model into the dynamic fuzzy clustering. Second, in order to model the observed dynamic texture data, only grayscale information is taken into consideration of the existing models. We use different visual properties by proposing a new distribution in this paper. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback–Leibler divergence information. Numerical experiments are presented, where the proposed model is tested on various simulated and real dynamic textures.