1. Dept. of Computer and Information Sciences
Vantage Objects
Dr. Rolf Lakaemper
Dept. of Computer and Information Sciences
Temple University

2. The Application: ISS Database
Task:
Create Image Database
Problem:
Response Time
Comparison of 2 Shapes: 23ms on Pentium1Ghz
ISS contains 15,000 images:
Response Time about 6 min.
Clustering not possible (not a metric)

3. Vantage Objects Solution:
Full search on entire database using a simpler
comparison
Vantage Objects (Vleugels / Veltkamp, 1999) provide a simple comparison of
n- dimensional vectors (n typically < 100)
Paper: Vleugels/Veltkamp:
Efficient Image Retrieval through Vantage Objects (1999)

4. Vantage Objects The Idea:
Compare the query-shape q to a predefined subset S of the shapes in the database D
The result is an n-dimensional Vantage Vector V,
n = |S| s1 v1 s2 v2 q s3 v3 … sn vn

5. Vantage Objects
- Each shape can be represented by a single Vantage Vector
- The computation of the Vantage Vector calls the ASR – comparison only n times
- ISS uses 54 Vantage Objects, reducing the comparison time (needed to create the Vantage Vector) to < 1.5s
- How to compare the query object to the database ?

6. Vantage Objects
- Create the Vantage Vector vi for every shape di in the database D
- Create the Vantage Vector vq for the query-shape q
- compute the euclidean distance between vq and vi
- best response is minimum distance
Note: computing the Vantage Vectors for the database objects is an offline process !

7. How to define the set S of Vantage Objects ?

8. k=1..i-1 e(di , sk) maximal. (e = eucl. dist.)
Vantage Objects
Algorithm 1 (Vleugels / Veltkamp 2000):
Predefine the number n of Vantage Objects
S0 = { }
Iteratively add shapes di  D\Si-1 to Si-1 such that
Si = Si-1  di
and
k=1..i-1 e(di , sk) maximal. (e = eucl. dist.)
Stop if i = n.

9. Vantage Objects
Result:
Did not work for ISS.

10. Algorithm 2 (Latecki / Henning / Lakaemper):
Vantage Objects
Algorithm 2 (Latecki / Henning / Lakaemper):
Def.:
A(s1,s2): ASR distance of shapes s1,s2
q: query shape
‘Vantage Query’ : determining the result r by minimizing e(vq , vi ) vi = Vantage Vector to si
‘ASR Query’: determining the result r by minimizing A(q,di )
Vantage Query has certain loss of retrieval quality compared to ASR query.
Define a loss function l to model the extent of retrieval performance

11. Vantage Objects
Given a Database D and a set V of Vantage Vectors, the loss of retrieval performance for a single query by shape q is given by:
lV,D (q) = A(q,r),
Where r denotes the resulting shape of the vantage query to D using q.
Property:
lV,D (q) is minimal if r is the result of the ASR-Query.

12. L(S) = 1/n  lS,D\{si} (si)
Vantage Objects
Now define retrieval error function L(S) of set
S={s1 ,…, sn }  D of Vantage Vectors of Database D:
L(S) = 1/n  lS,D\{si} (si)
Task:
Find subset S  D such that L(S) is minimal.

13. Vantage Objects Algorithm: V0={ }
iteratively determine sj in D\Sj-1 such that
Sj =Sj-1  sj and L(Vj) minimal.
Stop if improvement is low

14. Number of Vantage Objects
Result:
Worked fine for ISS, though handpicked objects stil performed better.
Handpicked
Algorithm 2
L(S)
Number of Vantage Objects

15. Vantage Objects
…some of the Vantage Objects used in ISS:

16. Vantage Objects helped in times of need, but discussion is required !