| The recent advances in the image data capture, storage and communication technologies have brought a rapid growth of image and video contents. Image and video data mining, the process of extracting hidden patterns from image and video data, becomes an important and emerging task. Despite a lot of previous work, data mining techniques that are successful in mining text and transaction data cannot simply apply to image and video data that are much more complex. Due to the structure and content variations of the visual patterns, it is not a trivial task to discover meaningful patterns from images and videos. This dissertation presents a systematic study on image and video data mining and concentrates on two fundamental problems: mining interesting patterns from image and video data and discovering discriminative features for visual pattern classification. Both of them are crucial for the research and applications of indexing, retrieving and understanding image and video contents.;For interesting pattern mining from image and video data, several effective data mining methods are developed for the discovery of different types of visual patterns, including common object discovery from images, semantically meaningful visual pattern discovery, and recurring pattern mining from videos. The difficulties of the structure and content variations for mining complex visual patterns are addressed and efficient algorithms are proposed. For feature mining, a data mining-driven approach is proposed based on frequent pattern mining. The relationship between frequent patterns and discriminative features is discussed and the conditions that frequent patterns can be discriminative features are presented. The discovered features are finally integrated through a multi-class boosting technique. Extensive experiments on image and video data have validated the proposed approaches. |
