Learning the "Epitome" of an Image
Estimating and visualizing high-order statistics of multivariate data is important for analysis, synthesis and visualization in science and engineering. Often, data consists of measurements on an underlying domain, such as space or time. Examples include images, audio signals and text, where the domains are 2-D space, 1-D time and 1-D symbol index. We introduce a model called the “epitome” that can simultaneously represent multi-scale high-order statistics as a set of parameters on the same domain as the input data. A cost function measures how well multi-scale patches drawn from the input data match the epitome and this cost function can be optimized efficiently using the EM algorithm. Our technique reduces a large number of high-order statistics to an intuitive, compact representation that is suitable for a variety of data processing applications. We demonstrate our method using problems of object detection, texture segmentation and image retrieval.