Searching by shape in heterogeneous Searching by shape in heterogeneous databases databases

IntroductionIntroduction AlgorithmsAlgorithms MethodologyMethodology ExperimentsExperiments Conclusions and future worksConclusions and future works

Searching criteriaSearching criteria

ColourColour

TextureTexture

Spatial relationshipsSpatial relationships

ShapeShape

Searching by shapeSearching by shape

Features:Features:

Rotation invariantRotation invariant

Translation invariantTranslation invariant

Scaling invariantScaling invariant

FastFast

Not adaptiveNot adaptive

First algorithmFirst algorithm

Features:Features:

It works on contoursIt works on contours

It is scalarIt is scalar

No feedbackNo feedback

Required invariants assuredRequired invariants assured

ParametersParameters

C

d1

d2

Distanze ordinate

Ampiezza

d1

d2

f(x)= ax3+ bx2+ cx+d

ConsiderationsConsiderations

Advantages:

Good result in a few cases

Very fast (only 4 parameters)

Rotation transaltion scaling invariance

Disadvantages:

Sensitivity to little local variations

Symmetric shapes make the algorithm collapse

Second algorithmSecond algorithm

C

0.25

0.5

0.75

1

0°

120°

60°

180°

240°

300°

1 ) Mass center is computed

2) Inertial axisi are computed

3) 4 annulus are plotted

4) 6 sector are plotted

Matrix generationMatrix generation

C

0.25

0.5

0.75

1

0°

120°

60°

180°

240°

300°

  0°-60°

60°-120°

120°-180°

180°-240°

240°-300°

300°-360°

00.25

           

0.250.5

           

0.50.75

           

0.751

           

d1

  0°-60°

60°-120°

120°-180°

180°-240°

240°-300°

300°-360°

00.25

           

0.250.5

           

0.50.75

           

0.751

           

d1

Matrix comparisonMatrix comparison

Query matrix Matrix image 2

Matrix image N

Matrix image 11

Images are

ranked

according to

similarity

PerformancePerformance

Precision & RecallPrecision & Recall

ProblemProblem

When an element is relevant?When an element is relevant?

We need a classification in the databaseWe need a classification in the database

The databaseThe database

Database of 4553 images by Corel DrawDatabase of 4553 images by Corel Draw

Heterogeneous images for size, subject and colourHeterogeneous images for size, subject and colour

We define 22 categories of different cardinality We define 22 categories of different cardinality

(from 54 to 400)(from 54 to 400)

Choice of categoriesChoice of categories

A trade off between:A trade off between:

Subdivision basing upon the shape of the objectSubdivision basing upon the shape of the object

I. e. simboli poligonali =Polygonal simbolsI. e. simboli poligonali =Polygonal simbols

Subdivision basing upon the semantic meaning of teh Subdivision basing upon the semantic meaning of teh

objects (i.e. flying objects)objects (i.e. flying objects)

ExperimentsExperiments

Different level of resolution (wavelets)Different level of resolution (wavelets)

20 query for each category and each resolution level20 query for each category and each resolution level

There is not a priviledged level There is not a priviledged level

of resolution for all classesof resolution for all classes

ExperimentsExperimentsSimboli Tondi

Cardinalità Precision5

Precision10

Precision15

Precision20

Precision25

Ideale 374 1 1 1 1 1

Imm. Base 374 0,644 0,595 0,523 0,533 0,541

Livello 1 374 0,660 0,639 0,602 0,557 0,529

Livello 2 374 0,540 0,441 0,432 0,454 0,455

Livello 3 374 0,550 0,426 0,434 0,426 0,424

Simboli a Scudo

Cardinalità Precision5

Precision10

Precision15

Precision20

Precision25

Ideale 301 1 1 1 1 1

Imm. Base 301 0,544 0,464 0,421 0,398 0,370

Livello 1 301 0,608 0,488 0,453 0,422 0,403

Livello 2 301 0,688 0,592 0,533 0,486 0,459

Livello 3 301 0,600 0,484 0,456 0,416 0,386

ExperimentsExperiments

Analysis of the results for each categoryAnalysis of the results for each category

More 20 queries for each category at the best More 20 queries for each category at the best resolutionresolution

Precision > 60%Precision > 60%

ExperimentsExperimentsCategorie Cardinalità

Precision5

Precision10

Precision15

Precision20

Precision25

A. Reali 300 0,330 0,270 0,213 0,200 0,180

A. Stilizzati 131 0,268 0,194 0,152 0,120 0,110

Automezzi 54 0,320 0,205 0,157 0,130 0,116

Case 81 0,240 0,153 0,110 0,093 0,090

Composizioni 247 0,330 0,265 0,223 0,190 0,176

Dinosauri 95 0,470 0,360 0,300 0,268 0,244

F. Atipiche 400 0,360 0,260 0,223 0,190 0,176

Frasi 101 0,240 0,145 0,123 0,103 0,094

Insetti 132 0,310 0,190 0,157 0,140 0,128

O. Allungati 145 0,660 0,520 0,427 0,380 0,360

O. Poligonali 391 0,490 0,355 0,294 0,273 0,242

O. Curvilinei 197 0,280 0,160 0,140 0,113 0,098

O. Volanti 332 0,490 0,335 0,294 0,273 0,242

Pers. Reali 200 0,460 0,330 0,287 0,283 0,276

Pers. Stilizzate 161 0,408 0,240 0,205 0,222 0,195

Pesci 164 0,376 0,248 0,207 0,188 0,178

Scene 123 0,240 0,133 0,107 0,095 0,088

S. Poligonali 361 0,454 0,304 0,276 0,238 0,220

S. Tondi 374 0,660 0,639 0,602 0,557 0,529

S. a Scudo 301 0,688 0,592 0,533 0,486 0,459

Uccelli 208 0,333 0,200 0,156 0,139 0,120

Visi 55 0,260 0,145 0,127 0,110 0,096

ExperimentsExperiments

Query:

1° 2° 3° 4° 5°

6° 7° 8° 9° 10°

Distanze

1°- 0

2°- 0,0803

3°- 0,0896

4°- 0,0909

5°- 0,1006

6°- 0,1039

7°- 0,1041

8°- 0,1070

9°- 0,1087

10°- 0,1117

ExperimentsExperiments

1° 2° 3° 4° 5°

6° 7° 8° 9° 10°

Query:

Distanze

1°- 0

2°- 0,0483

3°- 0,0710

4°- 0,0813

5°- 0,0871

6°- 0,0922

7°- 0,0927

8°- 0,0936

9°- 0,0938

10°- 0,0952

ExperimentsExperiments

1° 2° 3° 4° 5°

6° 7° 8° 9° 10°

Query:

Distanze

1°- 0

2°- 0,1289

3°- 0,1433

4°- 0,1506

5°- 0,1520

6°- 0,1545

7°- 0,1546

8°- 0,1578

9°- 0,1585

10°- 0,1594

DistancesDistances

0 50 100 150 200 250 300 350 400 450 5000

0.05

0.1

0.15

0.2

0.25

Number of images

Dis

tan

ces

ConclusionsConclusions

The method is:The method is:

FastFast

Acceptable precision for some classesAcceptable precision for some classes

Future worksFuture works

Upgrade of thealgorithmUpgrade of thealgorithm

Fusion with colour or texture methodsFusion with colour or texture methods