Computationally Efficient Stochastic Realization for Internal Multiscale Autoregressive Models
Multidimensional Systems and Signal Processing
Combining Belief Networks and Neural Networks for Scene Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Trees: Learning to Model Outdoor Scenes
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Image Modeling with Position-Encoding Dynamic Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous non-gaussian data clustering, feature selection and outliers rejection
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Learning a generative model of images by factoring appearance and shape
Neural Computation
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A class of multiscale stochastic models based on scale-recursive dynamics on trees has previously been introduced. Theoretical and experimental results have shown that these models provide an extremely rich framework for representing both processes which are intrinsically multiscale, e.g., 1/f processes, as well as 1D Markov processes and 2D Markov random fields. Moreover, efficient optimal estimation algorithms have been developed for these models by exploiting their scale-recursive structure. The authors exploit this structure in order to develop a computationally efficient and parallelizable algorithm for likelihood calculation. They illustrate one possible application to texture discrimination and demonstrate that likelihood-based methods using the algorithm achieve performance comparable to that of Gaussian Markov random field based techniques, which in general are prohibitively complex computationally