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In this paper we propose a novel image representation method that characterizes an image as a spatiogram--a generalized histogram--of colors quantized by Gaussian Mixture Models (GMMs). First, we quantize the color space using a GMM, which is learned by the Expectation-Maximization (EM) algorithm from the training images. The number of Gaussian components (i.e., the number of quantized color bins)...
A novel image segmentation method that combines spectral clustering and Gaussian mixture models is presented in this paper. The new method contains three phases. First, the image is partitioned into small regions modeled by a Gaussian Mixture Model (GMM), and the GMM is solved by an Expectation–Maximization (EM) algorithm with a newly proposed Image Reconstruction Criterion, named EM-IRC. Second,...
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