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Pictorial Query by Example PQBE

Pictorial Query by Example PQBE. Vida Movahedi Mar. 2007. Contents. Symbolic Images Direction Relations PQBE Implementation of queries using skeleton images Sample application Construction of Symbolic Images. Symbolic Image.

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Pictorial Query by Example PQBE

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  1. Pictorial Query by ExamplePQBE Vida Movahedi Mar. 2007

  2. Contents • Symbolic Images • Direction Relations • PQBE • Implementation of queries using skeleton images • Sample application • Construction of Symbolic Images

  3. Symbolic Image • Symbolic image: is an array representing a set of objects and a set of direction relations among them • Used in • Context-based retrieval in image databases • Spatial reasoning • Path planning • Image similarity retrieval

  4. Direction Relations • Primitive Direction relations: • {NorthWest, RestrictedNorth, NorthEast,RestrictedWest, SamePosition, RestrictedEast, SouthWest, RestrictedSouth, SouthEast} • Y: reference object • Direction relation of primary object • All primitives are transitive, SamePosition is symmetric

  5. Introducing PQBE • Pictorial Query-by-example • Generalizes from example given by user • Uses skeleton images (which are symbolic images) as queries • Ability to express negation, union, intersection, join, etc

  6. Description by Sets • O(I): objects of image I • C(I): primitive direction relations (constraints) between all pairs of objects in image I • Example: • O(u)={O, P, Q} • C(u)={RestrictedEast(Q,O),SouthWest(O,P), RestrictedNorth(P,Q)} • Note SamePosition and converse relations not included for simplicity u

  7. Query Queries with one skeleton image Database Image Constant _: variables (for objects/ images) are precede by ‘_’ P: printing character, when before an object variable/constant causes its value to be retrieved and displayed

  8. Query • A symbolic image I is a subimage of J iff O(I)O(J) & C(I)C(J) • Result of a query: set of all symbolic (sub)images that satisfy the spatial conditions imposed by sets O and C of skeleton images • Assumptions: closed world, domain closure, unique name

  9. Example: Query 1 Retrieve the subimages of s that contain an object X where X is NorthEast of B in s.

  10. Example: Query 2

  11. Example: Query 3 & 4

  12. Example: Queries 5-7

  13. Querying object configurations

  14. Querying relations between objects

  15. Union, Intersection, Join

  16. Queries with multiple images

  17. Queries with image retrieval

  18. Update Operations • P: printing character  Select • R: removing character  Delete • I: inserting character  Insert

  19. Application: Geographical Queries Maps of cities in Central Europe Symbolic Image A sample query

  20. Spatial Representation • preserve location in space • without incorporating information such as shape, size, texture, or color of objects • e.g. subway maps contain no information about the shapes of the stations

  21. Different Areas, Different Goals • Explanatory and predictive power • Computational models of Vision and Imagery • Expressive power and inferential adequacy • Artificial Intelligence representation schemes • Efficient manipulation of large amounts of geographic and geometric data • Spatial Databases

  22. Construction of 2D-G string Segmented Original Image Cutting function: detects and records differences in object projections on the x and y axis

  23. Construction of Symbolic Arrays

  24. References [1] Dimitris Papadias and Timos Sellis (1995), A Pictorial Query-By-Example Language, Journal of Visual Languages and Computing, vol. 6, pp. 53-72. [2] Dimitris Papadias and Timos Sellis (1994), Qualitative Representation of Spatial Knowledge in Two-Dimensional Space, Very Large Databases Journal, Special Issues on Spatial Databases, vol. 3, pp. 476-513.

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