Querying Objects Modeled by Arbitrary Probability Distributions

In many modern applications such as biometric identification systems, sensor networks, medical imaging, geology, and multimedia databases, the data objects are not described exactly. Therefore, recent solutions propose to model data objects by probability density functions(pdf). Since a pdf describing an uncertain object is often not explicitly known, approximation techniques like Gaussian mixture models(GMM) need to be employed. In this paper, we introduce a method for efficiently indexing and querying GMMs allowing fast object retrieval for arbitrary shaped pdf. We consider probability ranking queries which are very important for probabilistic similarity search. Our method stores the components and weighting functions of each GMM in an index structure. During query processing the mixture models are dynamically reconstructed whenever necessary. In an extensive experimental evaluation, we demonstrate that GMMs yield a compact and descriptive representation of video clips. Additionall...

• Type: Conference
• Year: 2007
• Where: SSD
• Authors: Christian Böhm, Peter Kunath, Alexey Pryakhin, Matthias Schubert
• Keywords: Data Objects, Probabilistic Similarity Search, Probability Ranking Queries, SSD 2007