Opportunistic Content Search of Smartphone Photos

Abstract

Photos taken by smartphone users can accidentally contain content that is timely and valuable to others, often in real-time. We report the system design and evaluation of a distributed search system, Theia, for crowd-sourced real-time content search of smartphone photos. Because smartphones are resource-constrained, Theia incorporates two key innovations to control search cost and improve search efficiency. Incremental Search expands search scope incrementally and exploits user feedback. Partitioned Search leverages the cloud to reduce the energy consumption of search in smartphones. Through user studies, measurement studies, and field studies, we show that Theia reduces the cost per relevant photo by an average of 59%. It reduces the energy consumption of search by up to 55% and 81% compared to alternative strategies of executing entirely locally or entirely in the cloud. Search results from smartphones are obtained in seconds. Our experiments also suggest approaches to further improve these results.