Abstract

Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network. With a large amount of visual data spread among different nodes, it is an important but challenging issue to perform content-based retrieval in peer-to-peer networks. Compared with central environments, the key challenge is to efficiently obtain a global codebook, as images are distributed across the whole peer-to-peer network. In addition, a peer-to-peer network often evolves dynamically, which makes a static codebook less effective for retrieval tasks. Therefore, I propose a dynamic codebook updating method by optimizing the mutual information between the resultant codebook and relevance information, and the workload balance among nodes that manage different code words. The broad experimental results point to that the future approach is scalable in budding and distributed peer-to-peer networks, while achieving improved revival accuracy.