Content-Based Image Retrieval through flexible technology in Peer-to-Peer Networks

Abstract

Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network. With a huge amount of visual records dispensed amongst distinct nodes, it is an important but difficult difficulty to carry out content-based totally retrieval in peer-to-peer networks. While maximum of the present methods cognizance on indexing high dimensional visible functions and feature limitations of scalability, on this paper we endorse a scalable approach for content-based totally photograph retrieval in peer-to-peer networks by employing the bag-of-visual words model. Compared with centralized environments, the key undertaking is to successfully acquire a international codebook, as photographs are dispensed throughout the entire peer-to-peer community. In addition, a peer-to-peer community often evolves dynamically, which makes a static codebook less powerful for retrieval responsibilities. Therefore, we advise a dynamic codebook updating approach by way of optimizing the mutual statistics between the consequent codebook and relevance information, and the workload balance amongst nodes that control different codewords. In order to in addition improve retrieval performance and decrease network value, indexing pruning techniques are advanced. Our comprehensive experimental consequences imply that the proposed technique is scalable in evolving and disbursed peer-to-peer networks, at the same time as accomplishing advanced retrieval accuracy.