Effective Retrieval and Visual Analysis in Multimedia Databases
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Based on advances in acquisition, storage, and dissemination technology, increasing amounts of multimedia content such as images, audio, video, or 3D models, become available. The Feature Vector (FV) paradigm is one of the most popular approaches for managing multimedia content due to its simplicity and generality. It maps multimedia elements from object space to metric space, allowing to infer object similarity relationships from distances in metric space. The distances in turn are used to implement similarity-based multimedia applications. For a given multimedia data type, many different FV mappings are possible, and the effectiveness of a FV mapping can be understood as the degree of resemblance of object space similarity relationships by distances in metric space. The effectiveness of the FV mapping is essential for any application based on it. Two main ideas motivate this thesis. We first recognize that the FV approach is promising, but needs attention of FV selection and engineering in order to serve as a basis for building effective multimedia applications. Secondly, we believe that visualization can contribute to building powerful user interfaces for analysis of the FV as well as the object space. This thesis focuses on supporting a number of important user tasks in FV-based multimedia databases. Specifically, we propose innovative methods for (a) effective processing of content-based similarity queries, (b) FV space visualization for discrimination analysis, and (c) visualization layout generation for content presentation. The methods are applied and evaluated on a number of specific multimedia data types such as 3D models, images, and time series data, and are expected to be useful in many other multimedia domains.