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, the authors introduce a method for efficiently indexing and querying GMMs allowing fast object retrieval for arbitrary shaped pdf.