{"id":327428,"date":"2016-11-27T16:07:24","date_gmt":"2016-11-28T00:07:24","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=327428"},"modified":"2018-10-16T20:24:52","modified_gmt":"2018-10-17T03:24:52","slug":"learning-mixture-models-images-inferring-spatial-transformations-using-em-algorithm","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-mixture-models-images-inferring-spatial-transformations-using-em-algorithm\/","title":{"rendered":"Learning Mixture Models of Images and Inferring Spatial Transformations Using the EM algorithm"},"content":{"rendered":"
Mixture modeling and clustering algorithms are effective, simple ways to represent images using a set of data centers. However, in situations where the images include background clutter and transformations such as translation, rotation, shearing and warping, these methods extract data centers that include clutter and represent different transformations of essentially the same data. 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