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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