Intelligent System For Content Based Image Retrieval And Segmentation

Large amounts of digital images are created and accessed daily by the public, academia, and corporations. Keyword indexing is useful but limited in describing image content. Intelligent content-based retrieval is a key technology to address this problem and to facilitate efficient image-based knowledge. This dissertation presents an attempt to improve image segmentation and region-based image retrieval utilizing artificial intelligence methods of probabilistic perspective to achieve this goal. Two novel systems are proposed: fuzzy-logic expert system for objects labeling OLFES and cluster-based retrieval system CoIRS. The two systems are based on probabilistic learning framework called EMIS and are integrated for image segmentation and retrieval. The EMIS is based on Expectation-Maximization (EM) algorithm that estimates Bayesian Maximum Likelihood parameters to fit data into Gaussian Mixture Model. The color and texture features of the image's small patches are fed to EM.