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Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 01/02 Podcast
Efficient Analysis in Multimedia Databases

Efficient Analysis in Multimedia Databases

Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 01/02 vor 19 Jahren

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The rapid progress of digital technology has led to a situation
where computers have become ubiquitous tools. Now we can find them
in almost every environment, be it industrial or even private. With
ever increasing performance computers assumed more and more vital
tasks in engineering, climate and environmental research, medicine
and the content industry. Previously, these tasks could only be
accomplished by spending enormous amounts of time and money. By
using digital sensor devices, like earth observation satellites,
genome sequencers or video cameras, the amount and complexity of
data with a spatial or temporal relation has gown enormously. This
has led to new challenges for the data analysis and requires the
use of modern multimedia databases. This thesis aims at developing
efficient techniques for the analysis of complex multimedia objects
such as CAD data, time series and videos. It is assumed that the
data is modeled by commonly used representations. For example CAD
data is represented as a set of voxels, audio and video data is
represented as multi-represented, multi-dimensional time series.
The main part of this thesis focuses on finding efficient methods
for collision queries of complex spatial objects. One way to speed
up those queries is to employ a cost-based decompositioning, which
uses interval groups to approximate a spatial object. For example,
this technique can be used for the Digital Mock-Up (DMU) process,
which helps engineers to ensure short product cycles. This thesis
defines and discusses a new similarity measure for time series
called threshold-similarity. Two time series are considered similar
if they expose a similar behavior regarding the transgression of a
given threshold value. Another part of the thesis is concerned with
the efficient calculation of reverse k-nearest neighbor (RkNN)
queries in general metric spaces using conservative and progressive
approximations. The aim of such RkNN queries is to determine the
impact of single objects on the whole database. At the end, the
thesis deals with video retrieval and hierarchical genre
classification of music using multiple representations. The
practical relevance of the discussed genre classification approach
is highlighted with a prototype tool that helps the user to
organize large music collections. Both the efficiency and the
effectiveness of the presented techniques are thoroughly analyzed.
The benefits over traditional approaches are shown by evaluating
the new methods on real-world test datasets.