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X. Feng, C.K.I. Williams, S.N. Felderhof, "Combining Belief Networks and Neural Networks for Scene Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 467483, April, 2002.  
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@article{ 10.1109/34.993555, author = {X. Feng and C.K.I. Williams and S.N. Felderhof}, title = {Combining Belief Networks and Neural Networks for Scene Segmentation}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {24}, number = {4}, issn = {01628828}, year = {2002}, pages = {467483}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.993555}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Combining Belief Networks and Neural Networks for Scene Segmentation IS  4 SN  01628828 SP467 EP483 EPD  467483 A1  X. Feng, A1  C.K.I. Williams, A1  S.N. Felderhof, PY  2002 KW  treestructured belief network (TSBN) KW  hierarchical modeling KW  Markov random field (MRF) KW  neural network KW  scaledlikelihood method KW  conditional maximumlikelihood training KW  Gaussian mixture model KW  expectationmaximization (EM) VL  24 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
We are concerned with the problem of image segmentation, in which each pixel is assigned to one of a predefined finite number of labels. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of label images. Following the work of, we consider the use of treestructured belief networks (TSBNs) as prior models. The parameters in the TSBN are trained using a maximumlikelihood objective function with the EM algorithm and the resulting model is evaluated by calculating how efficiently it codes label images. A number of authors have used Gaussian mixture models to connect the label field to the image data. In this paper, we compare this approach to the scaledlikelihood method of where local predictions of pixel classification from neural networks are fused with the TSBN prior. Our results show a higher performance is obtained with the neural networks. We evaluate the classification results obtained and emphasize not only the
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