Paper Info
Title
Demo abstract - MediaScope: Selective on-demand media retrieval from mobile devices
Abstract
Motivated by an availability gap for visual media, where images and videos are uploaded from mobile devices well after they are generated, we explore the selective, timely retrieval of media content from a collection of mobile devices. We envision this capability being driven by similarity-based queries posed to a cloud search front-end, which in turn dynamically retrieves media objects from mobile devices that best match the respective queries within a given time limit. Building upon a crowd-sensing framework, we have designed and implemented a system called MediaScope that provides this capability. MediaScope is an extensible framework that supports nearest-neighbor and other geometric queries on the feature space (e.g., clusters, spanners), and contains novel retrieval algorithms that attempt to maximize the retrieval of relevant information. From experiments on a prototype, MediaScope is shown to achieve near-optimal query completeness and low to moderate overhead on mobile devices.
Year
DOI
Venue
2013
10.1109/IPSN.2013.6917560
IPSN
Keywords
Field
DocType
crowd-sensing framework,media content,mobile devices,mobile handsets,selective on-demand media retrieval,crowd-sensing,timely retrieval,mediascope,mobile device,cloud search front-end,image-retrieval,mobile-device,novel retrieval algorithm,image sensors,image retrieval,feature-extraction,availability gap,extensible framework,similarity-based queries,dynamically retrieves media object,visual media,sensors,feature extraction,media
Mobile search,Feature vector,Information retrieval,Computer science,Upload,Image retrieval,Mobile database,Real-time computing,Feature extraction,Mobile device,Multimedia,Cloud computing
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
6
Name
Order
Citations
PageRank
Xing Xu1867.35
Yurong Jiang212611.36
Peter Terlecky3626.17
Tarek Abdelzaher410179729.36
Amotz Bar-Noy52986400.08
ramesh govindan6154302144.86