Fast retrieval of high-dimensional feature vectors in P2P networks using compact peer data summaries

Abstract

The retrieval facilities of most peer-to-peer (P2P) systems are limited to queries based on a unique identifier or a small set of keywords. The techniques used for this purpose are hardly applicable for content based image retrieval (CBIR) in a P2P network. Furthermore, we will argue that the curse of dimensionality and the high communication overhead prevent the adaptation of multidimensional search trees or fast sequential scan techniques for P2P CBIR. In the present paper we will propose two compact data representations that can be distributed in a P2P network and used as the basis for a source selection. This allows for communicating with only a small fraction of all peers during query processing without deteriorating the result quality significantly. We will also present experimental results confirming our approach.

Survey of research towards robust peer-to-peer networks: Search methods

“…Some have pointed to such work [60], but it made no explicit mention of the model [325]. One early paper on P2P contentbased image retrieval also leveraged the Bayesian model [326]. For the former two models, we briefly describe the design, then try to highlight robustness aspects.…”

A unified multimedia and semantic perspective for data retrieval in the semantic web

“…In [42] histograms summarizing the other peers' data are used at query time to determine the most promising peers. This approach is not scalable, since each peer has global knowledge of the network.…”

“…query routing, in the last few years much work has focused on this issue in P2P systems, with the aim of effectively and efficiently querying both semantic [24,25,[34][35][36][37][38][39][40][41] and content-searchable [29,30,32,33,[42][43][44][45] data.…”

System Design of a Super-Peer Network for Content-Based Image Retrieval

“…In [16,18], the peers are grouped according to their IDs not their contents. Each peer constructs its own summary and distributes it over its subnet using a rumor spreading technique that introduces communication and storage cost to all peers.…”