Image Data Source Selection Using Gaussian Mixture Models

Abstract

In peer-to-peer (P2P) networks, computers with equal rights form a logical (overlay) network in order to provide a common service that lies beyond the capacity of every single participant. Efficient similarity searchis generally recognized as a frontier in research about P2P systems. In literature, a variety of approaches exist. One of which is data source selection based approaches where peers summarize the data they contribute to the network, generating typically one summary per peer. When processing queries, these summaries are used to choose the peers (data sources) that are most likely to contribute to the query result. Only those data sources are contacted. In this paper we use a Gaussian mixture model to generate peer summaries using the peers' local data. We compare this method to other local unsupervised clustering methods for generating peer summaries and show that a Gaussian mixture model is promising when it comes to locally generated summaries for peers without the need for a distributed summary computation that needs coordination between peers.