# Searching by shape in heterogeneous databases Introduction Introduction Algorithms Algorithms Methodology Methodology Experiments Experiments Conclusions.

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Searching by shape in heterogeneous databases Introduction Introduction Algorithms Algorithms Methodology Methodology Experiments Experiments Conclusions and future works Conclusions and future works

Searching criteria Colour Texture Spatial relationships Shape

Searching by shape Features: Rotation invariant Rotation invariant Translation invariant Translation invariant Scaling invariant Scaling invariant Fast Fast Not adaptive Not adaptive

First algorithm Features: It works on contours It works on contours It is scalar It is scalar No feedback No feedback Required invariants assured Required invariants assured

Parameters C d1d1 d2d2 Distanze ordinate Ampiezza d1d1 d2d2 f(x)= ax 3 + bx 2 + cx+d

Considerations 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 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 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° 0 0.25 0.5 0.75 1 d1d1 0°- 60° 60°- 120° 120°- 180° 180°- 240° 240°- 300° 300°- 360° 0 0.25 0.5 0.75 1 d1d1

Matrix comparison Query matrixMatrix image 2 Matrix image N Matrix image 11 Images are ranked according to similarity

Performance Precision & Recall Problem When an element is relevant? We need a classification in the database

The database Database of 4553 images by Corel Draw Database of 4553 images by Corel Draw Heterogeneous images for size, subject and colour Heterogeneous images for size, subject and colour We define 22 categories of different cardinality (from 54 to 400) We define 22 categories of different cardinality (from 54 to 400)

Choice of categories A trade off between: Subdivision basing upon the shape of the object Subdivision basing upon the shape of the object I. e. simboli poligonali =Polygonal simbols I. e. simboli poligonali =Polygonal simbols Subdivision basing upon the semantic meaning of teh objects (i.e. flying objects) Subdivision basing upon the semantic meaning of teh objects (i.e. flying objects)

Experiments Different level of resolution (wavelets) Different level of resolution (wavelets) 20 query for each category and each resolution level 20 query for each category and each resolution level There is not a priviledged level of resolution for all classes

Experiments Simboli Tondi Cardinalità Precision 5 Precision 10 Precision 15 Precision 20 Precision 25 Ideale37411111 Imm. Base3740,6440,5950,5230,5330,541 Livello 13740,6600,6390,6020,5570,529 Livello 23740,5400,4410,4320,4540,455 Livello 33740,5500,4260,4340,4260,424 Simboli a Scudo Cardinalità Precision 5 Precision 10 Precision 15 Precision 20 Precision 25 Ideale30111111 Imm. Base3010,5440,4640,4210,3980,370 Livello 13010,6080,4880,4530,4220,403 Livello 23010,6880,5920,5330,4860,459 Livello 33010,6000,4840,4560,4160,386

Experiments Analysis of the results for each category Analysis of the results for each category More 20 queries for each category at the best resolution Precision > 60%

Experiments CategorieCardinalità Precision 5 Precision 10 Precision 15 Precision 20 Precision 25 A. Reali3000,3300,2700,2130,2000,180 A. Stilizzati1310,2680,1940,1520,1200,110 Automezzi540,3200,2050,1570,1300,116 Case810,2400,1530,1100,0930,090 Composizioni2470,3300,2650,2230,1900,176 Dinosauri950,4700,3600,3000,2680,244 F. Atipiche4000,3600,2600,2230,1900,176 Frasi1010,2400,1450,1230,1030,094 Insetti1320,3100,1900,1570,1400,128 O. Allungati1450,6600,5200,4270,3800,360 O. Poligonali3910,4900,3550,2940,2730,242 O. Curvilinei1970,2800,1600,1400,1130,098 O. Volanti3320,4900,3350,2940,2730,242 Pers. Reali2000,4600,3300,2870,2830,276 Pers. Stilizzate1610,4080,2400,2050,2220,195 Pesci1640,3760,2480,2070,1880,178 Scene1230,2400,1330,1070,0950,088 S. Poligonali3610,4540,3040,2760,2380,220 S. Tondi3740,6600,6390,6020,5570,529 S. a Scudo3010,6880,5920,5330,4860,459 Uccelli2080,3330,2000,1560,1390,120 Visi550,2600,1450,1270,1100,096

Experiments 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

Experiments 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

Experiments 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

Distances 050100150200250300350400450500 0 0.05 0.1 0.15 0.2 0.25 Number of images Distances

Conclusions The method is: Fast Fast Acceptable precision for some classes Acceptable precision for some classes Future works Upgrade of thealgorithm Upgrade of thealgorithm Fusion with colour or texture methods Fusion with colour or texture methods

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