Comparison of Image Similarity Queries in P2P Systems

TL;DR

This paper evaluates the feasibility of implementing image similarity search in unstructured P2P systems, demonstrating that high-end resources can support large-scale, complex image search applications.

Contribution

It analyzes the limitations of structured DHT-based systems for image similarity queries and explores unstructured P2P topologies suitable for such applications.

Findings

Unstructured P2P systems can support large-scale image search with high bandwidth.

High-end computers and links are sufficient for complex image search deployments.

Structured DHT approaches are not suitable for image similarity queries.

Abstract

Given some of the recent advances in Distributed Hash Table (DHT) based Peer-To-Peer (P2P) systems we ask the following questions: Are there applications where unstructured queries are still necessary (i.e., the underlying queries do not efficiently map onto any structured framework), and are there unstructured P2P systems that can deliver the high bandwidth and computing performance necessary to support such applications. Toward this end, we consider an image search application which supports queries based on image similarity metrics, such as color histogram intersection, and discuss why in this setting, standard DHT approaches are not directly applicable. We then study the feasibility of implementing such an image search system on two different unstructured P2P systems: power-law topology with percolation search, and an optimized super-node topology using structured broadcasts. We examine the average and maximum values for node bandwidth, storage and processing requirements in the percolation and super-node models, and show that current high-end computers and high-speed links have sufficient resources to enable deployments of large-scale complex image search systems.