Efficient content-based P2P image retrieval using peer content descriptions

Peer-to-peer (P2P) networks are overlay networks that connect independent computers (also called nodes or peers). In contrast to client/server solutions, all nodes offer and request services from other peers in a P2P network. P2P networks are very attractive in that they harness the computing power of many common desktop machines and necessitate little administrative overhead. While the resulting computing power is impressive, efficiently looking up data still is the major challenge in P2P networks. Current work comprises fast lookup of one-dimensional values (Distributed Hash Tables, DHT) and retrieval of texts using few keywords. However, the lookup of multimedia data in P2P networks is still attacked by very few groups. In this paper, we present experiments with efficient Content Based Image Retrieval in a P2P environment, thus a P2P-CBIR system. The challenge in such systems is to limit the number of messages sent, and to maximize the usefulness of each peer contacted in the query process. We achieve this by distributing peer data summaries over the network. Obviously, the data summaries have to be compact in order to limit the communication overhead. We propose an CBIR scheme based on a compact peer data summary. This peer data summary relies on cluster frequencies. To obtain the compact representation of a peer’s collection, a global clustering of the data is efficiently calculated in a distributed manner. After that, each peer publishes how many of its images fall into each cluster. These cluster frequencies are then used by the querying peer to contact only those peers that have the largest number of images present in one cluster given by the query. In our paper we further detail the various challenges that have to be met by the designers of such a P2P-CBIR, and we present experiments with varying degree of data replication (duplicates of images), as well as quality of clustering within the network.