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High-Performance Distributed Multimedia Computing

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News Broadcast - September 21, 2005 (see video1.wmv) Police investigating over 80.000 ... Pattern Anal. Mach. Intell. in press, 2006. Current & Future Work ... – PowerPoint PPT presentation

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Title: High-Performance Distributed Multimedia Computing


1
High-Performance Distributed Multimedia Computing
  • Frank Seinstra, Jan-Mark Geusebroek

MultimediaN (BSIK Project)
Intelligent Systems Lab AmsterdamInformatics
InstituteUniversity of Amsterdam
2
MultimediaN and DAS-3
3
MultimediaN and high-performance computing
Van Essen et al. Science 255, 1999.
4
A Real Problem, part 1
  • News Broadcast - September 21, 2005 (see
    video1.wmv)
  • Police investigating over 80.000 (!) CCTV
    recordings
  • First match found no earlier than 2.5 months
    after July 7 attacks

5
Image/Video Content Analysis
  • Lots of research benchmark evaluations
  • PASCAL-VOC (10,000 images), TRECVID (200 hours
    of video)
  • A Problem of scale
  • At least 30-50 hours of processing time per hour
    of video!
  • BeeldGeluid gt 20.000 hours of TV broadcasts per
    year
  • NASA gt over 850 Gb of hyper-spectral image data
    per day
  • London Underground gt over 120.000 years of
    processing !!!

6
High Performance Computing
  • Solution
  • Very, very large scale parallel and distributed
    computing
  • New Problem
  • Very, very complicated software

Solution tool to make parallel distributed
computing transparent to user
User
Wide-Area Grid Systems
7
Parallel-Horus Features (1)
  • Sequential programming

Parallel-Horus
Sequential API
Parallelizable Patterns
Seinstra et al., Parallel Computing,
28(7-8)967-993, August 2002
8
Parallel-Horus Features (2)
  • Lazy Parallelization

Seinstra et al., IEEE Trans. Par. Dist. Syst.,
15(10)865-877, October 2004
9
Extensions for Distributed Computing
  • Wide-Area Multimedia Services

Parallel Horus Client
Parallel Horus Server
Parallel Horus Servers
Parallel Horus Servers
Parallel Horus Client
  • User transparency?
  • Abstractions techniques?
  • Grid connectivity problems?

10
A Real Problem, part 2
LambdaRAM ??
may be time-critical!
11
Color-Based Object Recognition (1)

  • Our Solution
  • Place retina over input image
  • Each of 37 retinal areas serves as a receptive
    field
  • For each receptive field
  • Obtain set of local histograms, invariant to
    shading / lighting
  • Estimate Weibull parameters ß and ? for each
    histogram
  • Hence scene description by set of 37x4x3 444
    parameters

Geusebroek, British Machine Vision Conference,
2006.
12
Color-Based Object Recognition (2)
  • Learning phase
  • Set of 444 parameters is stored in database
  • So learning from 1 example, under single visual
    setting

a hedgehog
  • Recognition phase
  • Validation by showing objects under at least 50
    different conditions
  • Lighting direction
  • Lighting color
  • Viewing position

13
Amsterdam Library of Object Images (ALOI)
  • In laboratory setting
  • 300 objects correctly recognized under all (!)
    visual conditions
  • 700 remaining objects missed under extreme
    conditions only

Geusebroek et al., Int. J. Comput. Vis..
61(1)103-112, January 2005
14
Example Object Recognition
See also http//www.science.uva.nl/fjseins/aibo.
html
15
Example Object Recognition
(see video2.wmv)
Demonstrated live (a.o.) at ECCV 2006, June 8-11,
2006, Graz, Austria
16
Performance / Speedup on DAS-2
Single cluster, client side speedup
Four clusters, client side speedup
  • Recognition on single machine /- 30 seconds
  • Using multiple clusters up to 10 frames per
    second
  • Insightful even distant clusters can be used
    effectively for close to real-time recognition

17
Results applicability
  • Beneficial
  • Performance gains largely obtained for free
  • With Parallel-Horus we can build similar complex
    Grid applications in a matter of hours

18
Current Future Work
  • Very Large-Scale Distributed Multimedia
    Computing
  • Overcome practical annoyances
  • Software portability, firewall circumvention,
    authentication,
  • Optimization and efficiency
  • Tolerant to dynamic Grid circumstances,
  • Systematic integration of MM-domain-specific
    knowledge,
  • Deal with non-trivial communication patterns
  • Heavy intra- inter-cluster communication,
  • Reach the end users
  • Programming models, execution scenarios,
  • Collaboration with VU (Prof. Henri Bal) GridLab
  • Ibis www.cs.vu.nl/ibis/
  • Grid Application Toolkit www.gridlab.org

19
Conclusions
  • Effective integration of results from two largely
    distinct research fields
  • Ease of programming gt quick solutions
  • With DAS-3 / StarPlane we can start to take on
    much more complicated problems
  • But most of all
  • DAS-3 very significant for future MM research

20
The End
(see video3.avi)
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