Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sensor ranking: A primitive for efficient content-based sensor search
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
PhotoNet: A Similarity-Aware Picture Delivery Service for Situation Awareness
RTSS '11 Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium
Medusa: a programming framework for crowd-sensing applications
Proceedings of the 10th international conference on Mobile systems, applications, and services
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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 MScope that provides this capability. MScope 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, MScope is shown to achieve near-optimal query completeness and low to moderate overhead on mobile devices.