Image Segmentation Based on Expectation-Maximization Algorithm

Blobworld: Image segmentation based on the Expectation-Maximization algorithm and its application in image retrieval systems. Blobworld is an image segmentation algorithm designed to partition images into distinct regions for more effective retrieval and matching in subsequent image queries. The algorithm employs the Expectation-Maximization (EM) principle, iteratively estimating parameters to maximize the continuity and consistency of similar regions within an image. In implementation, the EM algorithm typically involves two key phases: the E-step computes posterior probabilities using current parameter estimates, while the M-step updates parameters to maximize expected log-likelihood. For image segmentation, this translates to grouping pixels based on color, texture, and spatial features through Gaussian mixture models. In image retrieval applications, Blobworld's region-based approach enables more accurate and efficient image matching by comparing segmented blobs rather than entire images, significantly improving retrieval performance through localized feature analysis and similarity computation.