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Constraints for Multimedia Presentation Generation

Explore the Cuypers multimedia generation engine and constraints in multimedia presentation generation. Learn about qualitative and quantitative constraints, backtracking, and automatic multimedia generation. Discover how constraints drive the creation of adaptive multimedia content efficiently.

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Constraints for Multimedia Presentation Generation

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  1. Constraints for Multimedia Presentation Generation Joost Geurts, Multimedia and Human-Computer Interaction CWI Amsterdam email: Joost.Geurts@cwi.nl

  2. Talk overview • Generating multimedia automatically • Cuypers multimedia generation engine • Multimedia and constraints • Quantitative constraints • Qualitative constraints • Cuypers demo • Conclusion, future directions

  3. Multimedia Presentation • Multimedia Presentation • Image, Text, Video, Audio • Based on Temporal and Spatial Synchronization • Multimedia Document • SMIL, SVG, HTML • WYSIWYG • Static Content • Problem: Dynamic Content

  4. Generating adaptive multimedia • Content • Large multimedia database • System profile • PC, PDA, WAP • Network profile • Modem, Gigabit • User profile • Language, Interests, Abilities, Preferences Too costly to author manually

  5. Cuypers multimedia generation engine

  6. Cuypers multimedia generation engine • Cuypers is based on • media independent presentation abstractions • transformation rules with built-in backtracking andconstraint solving

  7. Semantic structure Author does not specify complete presentation……but only rhetoric relations

  8. Communicative Devices …rhetoric relations are than transformed into presentation independent communicative devices…

  9. Automatic multimedia generation • Designer does not specify complete presentation……but only specifies requirements • System automatically finds a solution which meets requirements • How should the requirements be specified? • Declarative constraints

  10. Constraint satisfaction • Constraints occur often in our daily lives • Agenda, Travelling, Shopping • Constraint paradigm for Problem Solving • Declarative Used for problems with: • Many variables • Large domains • Based on domain reduction paradigm

  11. Intelligent reduction of possible values X  {1,2,3,4,5}, Y  {1,2} ; X  Y • X  {1,2}, • Y  {1,2} ; • X  Y

  12. Traditional use of constraints Quantitative constraints • Integer domain • Reduction by arithmetic relations • Greater than (>) • Less than (<) • Equals (=) • Example (x < y ; x  [0..10], y  [5..10] ) (x  + y  = z 3 , x = u  + 1 ; x   , y   , z  , u   )

  13. Solving a Constraint Satisfaction Problem • Problem SEND + MORE = MONEY • Modeling 1000 x S + 100 x E + 10 x N + D + 1000 x M + 100 x O + 10 x R + E = 10000 x M + 1000 x O + 100 x N + 10 x E + Y • Domain reduction / Search • Solution S=9, E=5, N=6, D=7, M=1, O=0, R=8, Y=2

  14. Quantitative Constraints in Multimedia …Communicative devices generate constraint-graph which the system tries to satisfy…

  15. Drawbacks of quantitative constraints • Too many (trivial) solutions that differ by: • 1 pixel position, or • 1 milliseconds in timing • Not sufficiently expressive • cannot specify “no overlap” constraint • Too low level • A.X2  B.X1

  16. Allen’s 13 temporal relations Allen’s relations are used for both spatial and temporal lay-out

  17. Solution: qualitative constraints • For non-typical domains • Boolean, • Three valued logics, • Allen’s relation • Advantages for Multimedia generation: • More intuitive • More expressive • Smaller domains

  18. Domain Reduction Rules • Inverse A before B  B after A A equal B  B equal A • Transitive A before B , B before C  A before C A overlaps B, B during C  A overlap C or A during C or A starts C • Equals A overlap C, A [o,d,s] C  A overlap C

  19. Qualitative Constraints …Qualitative solutions translate automatically to lower level quantitative constraints…

  20. New problem: What if constraints are insoluble? • Combine Prolog unification and backtracking with constraint solving • Use Prolog rules to generate constraints • Backtrack when constraints are insoluble Solution: Constraint Logic Programming

  21. Cuypers generation engine • Multiple layers: • Communicative devices generate constraints • Qualitative constraints translate to quantitative constraints • Solution of both constraints provides sufficient information for final presentation

  22. Cuypers demo: scenario • User is interested in Rembrandt and wants to • know about about the “chiaroscuro” technique • Query database • Generate constraints according to: • System profile • User profile • Network profile • Solve constraints / revise constraints • Generate SMIL presentation • Play presentation • Client • Server • Server • Server • Server • Client

  23. Conclusions • Quantitative constraintsare insufficient for automatic multimediapresentation generation. Also need • Qualitative constraintsto allow intuitive and effectivehigh level specification, and • Backtrackingfor revising specific constraintswhich otherwise cause the entire set to fail

  24. Discussion • Labeling • Choice of candidate variable • Choice of candidate value • Transitive Reasoning Rule • Infer implicit relations • Redundant • Allen’s Relations • Not very well suited for generating MM • Non interactive

  25. Future directions • Best-first instead of depth-first • Choose “best” among possible solutions • Needs evaluation criteria • Improve knowledge management • Make design knowledge declarative and explicit • Preserve metadata in final presentation • Use standardized and reusable profiles

  26. Thank you

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