Pictorial Query by Example PQBE. Vida Movahedi Mar. 2007

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1 Pictorial Query by Example PQBE 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 NorthEastRestrictedWest 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 OI: objects of image I CI: primitive direction relations constraints between all pairs of objects in image I Example: Ou={O P Q} Cu={RestrictedEastQOSouthWestOP RestrictedNorthPQ} Note SamePosition and converse relations not included for simplicity u

7 Queries with one skeleton image Database

8 Queries with one skeleton image Database Query

9 Queries with one skeleton image Database Image Constant Query

10 Queries with one skeleton image Database Image Constant _: variables for objects/ images are precede by _ Query

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

12 Query A symbolic image I is a subimage of J iff OI OJ & CI CJ Result of a query: set of all symbolic subimages that satisfy the spatial conditions imposed by sets O and C of skeleton images Assumptions: closed world domain closure unique name

13 Example: Query 1 Retrieve the subimages of s that contain an object X where X is NorthEast of B in s. {} } { = = I C s C B X NorthEast s O X X I O

14 Example: Query 1 Retrieve the subimages of s that contain an object X where X is NorthEast of B in s. {} } { = = I C s C B X NorthEast s O X X I O

15 Example: Query 2 O I = { X Y NorthEast X Y C s} C I = {}

16 Example: Query 2 O I = { X Y NorthEast X Y C s} C I = {}

17 Example: Query 3 & 4

18 Example: Query 3 & 4

19 Example: Query 3 & 4

20 Example: Query 3 & 4

21 Example: Queries 5-7

22 Example: Queries 5-7

23 Example: Queries 5-7

24 Example: Queries 5-7

25 Example: Queries 5-7

26 Example: Queries 5-7

27 Querying object configurations } { } { Y Z NE Y W RN Z W NW Y X NW Z X RW W X SW I C J C Y Z NE J C Y W RN J C Z W NW J C Y X NW J C Z X RW J C W X SW J Z W Y X I O = =

28 Querying object configurations } { } { Y Z NE Y W RN Z W NW Y X NW Z X RW W X SW I C J C Y Z NE J C Y W RN J C Z W NW J C Y X NW J C Z X RW J C W X SW J Z W Y X I O = =

29 Querying relations between objects

30 Querying relations between objects

31 Querying relations between objects

32 Querying relations between objects

33 Querying relations between objects

34 Querying relations between objects

35 Union Intersection Join

36 Union Intersection Join

37 Union Intersection Join

38 Union Intersection Join

39 Union Intersection Join

40 Union Intersection Join

41 Queries with multiple images

42 Queries with multiple images

43 Queries with multiple images

44 Queries with multiple images

45 Queries with image retrieval

46 Queries with image retrieval

47 Queries with image retrieval

48 Queries with image retrieval

49 Queries with image retrieval

50 Queries with image retrieval

51 Update Operations P: printing character Select R: removing character Delete I: inserting character Insert

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

53 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

54 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

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

56 Construction of Symbolic Arrays

57 References [1] Dimitris Papadias and Timos Sellis 1995 A Pictorial Query-By-Example Language Journal of Visual Languages and Computing vol. 6 pp [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

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