| With advances in the computer technologies and the advent of the World Wide Web, there has been an explosion in the amount and complexity of digital data being generated, stored, transmitted, analyzed, and accessed. Much of this information is multimedia in nature, including digital images, video, audio, graphics, and text data. In order to make use of this vast amount of data, efficient and effective techniques to analyze and retrieve multimedia information based on its content need to be developed.; This thesis is dedicated to the two of the most important media types: images and videos. Novel approaches to feature analysis, content representation, indexing and retrieval are presented.; The main contributions of this thesis axe: (i) reliable and robust feature extraction and representation techniques for images and videos; (ii) introduction of relevance feedback techniques to image retrieval, which greatly improves the retrieval performance and alleviates the user's query formulation burden; and (iii) introduction of a video hierarchical structure and construction of video Table of Contents to facilitate the video analysis and retrieval. Extensive experimental results over large test data sets have validated the proposed approaches. |
