Multimedia data ranging from images to videos and time series is created innumerous scientific, commercial and home applications. Access to increasinglylarge data volumes stored in multimedia databases is a core task toretrieve similar objects or to generate an overview of the entire content. Examplesinclude retrieval of similar magnetic resonance images for diagnosticpurposes, or automatic detection of customer segments for sales promotion.Meaningful retrieval and pattern detection require content-based methodsthat describe the relevant characteristics of multimedia objects. As opposedto manual keyword annotation techniques that are typically infeasible forlarge data volumes, content-based approaches use similarity models to processmultimedia data. Similarity models specify appropriate features andtheir relationship for effective content based access.As most multimedia features require many different attributes, high dimensionalityof multimedia features and huge database sizes are major challengesfor efficient and effective retrieval and mining.In this work, very common feature types for multimedia data are studied:histogram and time series data. Histograms are used for a variety offeatures such as color, shape or texture. Time series data is prevalent forsensor measurements, stock data, and may even be applied to shapes andother features as well. For these data types, effective adaptable similarity3models are usually computationally far too complex for usage in large highdimensional multimedia databases. Therefore efficient algorithms for theseeffective models are proposed.In this work, indexing techniques are used that allow for efficient queryprocessing and mining by restricting the search space to task relevant data.Multistep filter-and-refine approaches using novel filter functions with qualityguarantees ensure that fast response times are achieved without any loss ofresult accuracy.This thesis is structured as follows: first, in the Preliminaries, an overviewover the thesis and the major challenges in multimedia retrieval and miningis given. Part I discusses histogram retrieval, Part II studies time series retrieval.In Part III, efficient and effective histogram data mining is proposed,and Part IV presents novel time series mining techniques. Finally, this workis concluded and future research directions are suggested.<br/
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