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Proceedings Chapter

Reference

Learning features weights from user behavior in Content-Based Image Retrieval

MULLER, Henning, et al.

Abstract

This article describes an algorithm for obtaining knowledge about the importance of features from analyzing user log files of a content-based image retrieval system (CBIRS). The user log files from the usage of the Viper web demonstration system a re analyzed over a period of four months. Within this period about 3500 accesses to the system were made w ith almost 800 multiple image queries. All the actions of the users were logged in a file. The analysis only includes multiple image queries of the system with positive and/or negative input images, because only multiple image q ueries contain enough information for the method described.

Features frequently present in images marked together positively in the same que ry step get a higher weighting, whereas features present in one image marked positively and an other image marked negatively in the same step get a lower weighting. The Viper system offers a very large number of simple features. This allows the creation of flexible feature weightings with high values for importan t and low values for less important features. These weightings for features can of course differ [...]

MULLER, Henning, et al . Learning features weights from user behavior in Content-Based Image Retrieval. In: S. J. Simoff and O. R. Zaiane. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Workshop on Multimedia Data Mining MDM/KDD2000) . 2000.

Available at:

http://archive-ouverte.unige.ch/unige:47850

Disclaimer: layout of this document may differ from the published version.

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Learning Feature Weights from User Behavior in Content-Based Image Retrieval

Henning M ¨uller, Wolfgang M ¨uller, St ´ephane Marchand-Maillet, Thierry Pun

Computer Vision Group, University of Geneva 24 Rue du G ´en ´eral Dufour,

CH-1211 Gen `eve 4, Switzerland

henning.mueller@cui.unige.ch

David McG Squire

Computer Science and Software Engineering Monash University

Melbourne, Australia

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(5)

4. EXPERIMENTAL RESULTS

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0 0.2 0.4 0.6 0.8 1

0 0.2 0.4 0.6 0.8 1

Precision

;

Recall

Version with factor=1 With learnt factor of TSR database With learnt factor of all databases

< ò$ÿ4øŠôíuî!ï6=>@?AgôêB:ð}÷Eú4ô/ê[ûüŠûöí!ýC/ò$öð‰ê4õlñD/ò$ö-ð‹úøŠö

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Precision

;

Recall

Version with factor=1 With learnt factor of TSR database With learnt factor of all databases

< ò$ÿøŠôíNMUïO=>?AgôêB:ð^÷Eú4ô/êeûü‹ûöíýC/ò$ö-ð‰ê!õlñPZò$ö-ðŠúø‹ö

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0 0.2 0.4 0.6 0.8 1

Precision

;

Recall

With learnt factor 1 of TSR database With learnt factor 2 of TSR database With learnt factor 1 of all databases With learnt factor 2 of all databases

< ò$ÿøŠôíSUïThúýNBŠê!ôòrûúõeú!÷?ö-ð‹ígöhúñlòzó?í+ôíõöUhí'ò$ÿðö-òrõŠÿ

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(6)

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0 0.2 0.4 0.6 0.8 1

Precision

;

Recall

With learnt factor 1 of TSR database With learnt factor 2 of TSR database With learnt factor 1 of all databases With learnt factor 2 of all databases

<

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5. CONCLUSIONS AND FURTHER WORK

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6. REFERENCES

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Y[Z9Z\L]-^9^9_`[ab9bIc-d:e9f[dghjiclk9fm]nn9n9nK^poqrs^9t9h:uvKwLcxY[Zyizt

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†-ˆhFN4F? 4+41„[NK+0!&0&.&.‹N4j!5'"

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|hŸ—EŸ\­Ÿ©WO©kŸ–4¼8—$ª!WwW­h¡—E¦qŸ(V\VWŸXOpbb&b'N

†`ˆ*·A§?§P§‚1¦X ©Aª¦(Vs¦–ƒP¦–—=WO–!—ƒ¡=­(Ÿ©W¼X„“(“(WO©©h¦AÊ·(É/Ÿ¢;W

Ÿ–¼sS‹T…¼;W¦…{:Tƒ­XŸX(TEWO©@†‡ƒŠ«ˆ„%·'Sz{‰Š9Š‹p!t! 2DHp

DH& (p4™>Kjpç&.!&q‡Å&Å&Å+N

†ŽˆkDH& /04m,0!&N

Y9Z9Z\L]Œ^^[ghjis\_[b9bIc(vKZsgIc(vZ p:b&b&b+N

†a-ˆGON!çNDH-|p4„[N4³QN„1 -pUKN!„[NŒL /&+.!!' p'4q²:NµkN

£2Nl &%A!/48!U

vgKŽ9e9w[Z9df

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