Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Communication-Efficient Multi-view Keyframe Extraction in Distributed Video Sensors
Proceedings of IEEE Visual Communications and Image Processing Conference (VCIP'14), December 2014.
Shun-Hsing Ou, Yu-Chen Lu*, Jui-Pin Wang*, Shao-Yi Chien, Shou-De Lin*,
Mi-Yen Yeh^, Chia-Han Lee^, Phillip B. Gibbons†, V. Srinivasa Somayazulu†,
Graduate Inst. of EE & Dept of EE, National Taiwan University
* Dept of Computer Science and Information Engineering, Nat'l Taiwan University
^ Academia Sinica, Taiwan
† Intel Cooperation
Video sensors are widely used in many applications such as security monitoring and home care. However, the growth of the number of sensors makes it impractical to stream all videos back to a central server for further processing, due to communication bandwidth and server storage constraints. Multiview video summarization allows us to discard redundant data in the video streams taken by a group of sensors. All prior multi-view summarization methods, however, process video data in an off-line and centralized manner, which means that all videos are still required to be streamed back to the server before conducting the summarization. This paper proposes an on-line, distributed multi-view summarization system, which integrates the ideas of Maximal Marginal Relevance (MMR) and MS-Wave, a bandwidth-efficient distributed algorithm for finding k-nearestneighbors and k-farthest-neighbors. Empirical studies show that our proposed system can discard redundant videos and keep important keyframes as effectively as centralized approaches, while transmitting only 1/6 to 1/3 as much data.
FULL PAPER: pdf