farah prsentatation gvip 14 juin 2008

Satellite Image Retrieval Based On Ontology Merging

Imed Riadh Farah(1,2), Wassim Messaoudi(1,2),Karim saheb ettabâa (1,2)and Basel Solaiman(2)

(1) RIADI Laboratory, ENSI, Manouba University, Tunis, Tunisia(2) ITI Laboratory, Telecom Bretagne, France

Outline

• Context and problematic• State of the art : Satellite image retrieval• Our contribution

– Ontological modeling– Ontological model merging– Satellite image Retrieval

• Conclusion

03/05/23Satellite Image Retrieval Based On Ontology Merging

Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman

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Context and problematic



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RETRIVE ?

Satellite image baseSatellite image base

State of the art : satellite image retrieval

• Text-based metadata image retrieval

• Content-based image retrieval

Semantic image retrieval



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• Relevant feed back approach– Bring user in the retrieval process :

• The system provides initial retrieval results• the user judges the above results by selecting the

accepted results• Then, a machine learning algorithm is applied to learn

the user feedback



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• Machine Learning

Associate low-level features with query concepts.• Neural network for concept learning [Town et al 01]• Bayesian network for image classification [Vailaya et al 01]• SVM for image annotation

• Semantic Template– Support high-level image retrieval [Rui et al 99, Smith et al 98]

– Creating a map between high-level concept and low-level visual features.

• Example : Semantic Visual Template [Chang et al 98]



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• Ontology-based approach– Define high-level concepts– Representing of image content [Ruan et al 06, Zheng et al 03]



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Our Contribution

• Objectives

– Describe the semantic image content– Manage uncertain information– Retrieve satellite images



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Region Extraction

Ontological Modeling

Ontological Model Merging

Satellite images

Ontological Model 1

Ontological Model 2

Ontological Model 3

Merged ontological model

MOD

ULE

1 : O

NTOL

OGIC

AL M

ODEL

MOD

ELIN

G AN

D M

ERGI

NG

Region Extraction

• Satellite Image Segmentation– Partitioning an image into no overlapping regions that are homogeneous with

regards to some characteristics such as spectral and texture.

• Normalized cut• Edgeflow• Variational image decomposition• Split and merging• K-means• Fuzzy C-means• Etc.



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Region Extraction



Satellite images

Sensor O.M.

Scene O.M.

Spatial Relation O.M.

Semantic strategic Image Retrieval

Region Extraction



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Satellite image 1

Satellite image N

Region Extraction



Satellite images

Sensor O.M.

Scene O.M.




• Ontology – Specification of a conceptualization [Gruber 1993].

Knowledge representationExtendibility and reusabilityA higher degree of abstraction

• An ontology O is a 4-tuple (C,R,I,A), where – C : set of concepts– R : set of relations– I : set of instances – A : is a set of axioms

• Ontology language – XOL, OIL, DAML+OIL, RDF, OWL, OKBC, Ontolingua, etc



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Region Extraction



Satellite images

Sensor O.M.

Scene O.M.



Sensor Ontological Model



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Sensor

Active Passive

OpticRadar

OWL model:

<owl:Class rdf:ID="Sensor"/><owl:Class rdf:ID="Active"> <rdfs:subClassOf rdf:resource="#Sensor"/> </owl:Class><owl:Class rdf:ID="Passive"> <rdfs:subClassOf rdf:resource="#Sensor"/> </owl:Class><owl:Class rdf:ID="Optic"> <rdfs:subClassOf rdf:resource="#Passive"/> </owl:Class><owl:Class rdf:ID="Radar"> <rdfs:subClassOf rdf:resource="#Active"/> </owl:Class>

Region Extraction



Satellite images

Sensor O.M.

Scene O.M.



Scene Ontological Model



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Urban zone

Scene

Terrestrial zone Humid zone

Mountain

Communication ways

Energy lineBridge Road Railway

ParcelConstruction Forest River

Lac

Sea

Cultivate parcel Uncultivated parcel

Canal

Region Extraction



Satellite images

Sensor O.M.

Scene O.M.



Spatial Relation ontological Model



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Relation spatiale

At the right

At the left

Distance relation

On

Direction relation

Postion relation

Topologic relation

underbetween

FarNear

Disjunction relation

Inclusion relation

Adjacency relation

Region Extraction



Satellite images

Sensor O.M.

Scene O.M.




• Ontology Merging

• Approaches : – ONION, PROMPT, FCA-MERGE, Etc.

Don’t manage information uncertainty



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Incompletes ontological model

Merged model

MERGING

Region Extraction



Satellite images

Sensor O.M.

Scene O.M.



OWL probabilistic model



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For each instance in O1 and O2 If (Instance exists in O1 and not in O2) Or (Instance exists in O2 and not in O1) Then

Add Instance to M Else //(Instance not exists in tow models)

If (Instance has the same probability value in the two models O1 and O2) Then Add Instance to M Else //(Instance has different probability value) Apply the probabilistic method Add the accepted Instance.

End IfEnd

Union + Intersection + Uncertainty management

Region Extraction



Satellite images

Sensor O.M.

Scene O.M.



OWL probabilistic model



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Modèle O1 <Road> <Nom>R</Nom> <Probability>0.2</Probability> </Road> <River> <Nom>R</Nom> <Probability>0.8</Probability> </River><Cultivated zone> <Nom >Zone agricole</Nom> <Superficie> 500 Ha </Superficie> </Cultivated zone> <Urbain zone> <Nom >ZU1</Nom> <Area> 10 Ha </Area> </Urbain zone>

Modèle O2<Road> <Name>R</Name> <Probability>0.4</Probability></Road><River> <Name>R</Name> <Probabilité >0.6</Probabilité></River><Lake> <Name>Lac_de_Bizerte</Name> <area> 300 m3 </area></Lake><Urbain zone> <Nom >ZU1</Nom> <Area> 10 Ha </Area> </Urbain Zone>

Modèle M<Road> <Name>R</Name> <Probability>0.3</Probability></Road><River> <Name>R</Name> <Probability >0.7</Probability></River><cultivated zone> <Nom >Zone agricole</Nom><Area> 500 Ha </Area> </cultivated zone><Lake> <Nom Lac_de_Bizerte</Nom> <Area> 300 m3 </Area></Lake><Urbain Zone> <Nom >ZU1</Nom> <Area> 10 Ha </Area> </Urbain Zone>

+

Region Extraction



Satellite images

Sensor O.M.

Scene O.M.





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Merged ontological model

Similarity MeasureBase of

Ontological Models

Similar Satellite images

MOD

ULE

2 : S

TRAT

EGIC

IMAG

E RE

TRIE

VAL

Similar Ontological Models

Similarity Measure

• Terminological measure – Syntactic : String Matching [Maedche et al 02]

– Linguistic : Word-Net (S-Match)• Structural measure :semantic cotopy [Maedche et al 02] :

SC(Ci,H) ={CjA|H(Ci,Cj) v H(Cj,Ci)} : super and sub concepts of C



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|))2H{L}),(((22))1H{L}),(((1

1|

|))2H{L}),(((22))1H{L}),(((1

1|O2)O1,(L,TO'

FSCFFSCF

FSCFFSCF

Example



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Scene 1


MountainParcel

River

Cultivate parcelM

CP1

R

CP2

Scene 2


MountainParcel

Cultivate parcelM

CP1

Lac

L

Conclusion

• We presented an ontology based approach for retrieving satellite image retrieval.

• Our approach attempts to : – improve the quality of image retrieval– Describe the semantic content of the satellite

image– Manage uncertainty– Provide an automatic solution for efficient satellite

image retrieval.



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References



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[Rui et al 98] Y. Rui, T.S. Huang, M. Ortega, S. Mehrotra, Relevance feedback: a power tool for interactive content-based image retrieval, IEEETrans. Circuits Video Technol. 8 (5) (1998) 644–655.

[Rui et al 2000] Y. Rui, T.S. Huang, Optimizing learning in image retrieval, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, June 2000, pp. 1236–1243.

[Rui et al 99] Y. Rui, T.S. Huang, S.-F. Chang, Image retrieval: current techniques, promising directions, and open issues, J. Visual Commun. Image Representation 10 (4) (1999) 39–62.

[Smith et al 98] J.R. Smith, C.-S. Li, Decoding image semantics using composite region templates, IEEEWorkshop on Content-Based Access of Image and Video Libraries (CBAIVL-98), June 1998, pp. 9–13.

[Chang et al 98] S.F. Chang, W. Chen, H. Sundaram, Semantic visual templates: linking visual features to semantics, International Conference on Image Processing (ICIP), Workshop on Content Based Video Search and Retrieval, vol. 3, October 1998, pp. 531–534.

[Vailaya et al 01] A. Vailaya, M.A.T. Figueiredo, A.K. Jain, H.J. Zhang, Image classification for content-based indexing, IEEE Trans. Image Process.10 (1) (2001) 117–130.

[Town et al 01] C.P. Town, D. Sinclair, Content-based image retrieval using semantic visual categories, Society for Manufacturing Engineers, Technical Report MV01-211, 2001.

[Cai et al 04] D. Cai, X. He, Z. Li, W.-Y. Ma, J.-R. Wen, Hierachical clustering of WWWimage search results using visual, textual and link information, Proceedings of the ACM International Conference on Multimedia, 2004.

[Ruan et al 06] N. Ruan, N. Huang, W. Hong, “Semantic-Based Image Retrieval in Remote Sensing Archive: An Ontology Approach”, Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006, pages 2903-2906.

[Hyvönen et al 02] E. Hyvönen, A. Styrman, and S. Saarela. “Ontology-based Image Retrieval”, HIIT Publications Number 2002-03, pages 15-27.

[Kong et al 05] H. Kong, M. Hwang, P. Kim, "The Study on the Semantic Image Retrieval based on the Personalized Ontology", IEEE, 2005.

[Zheng et al 03] W. Zheng, Y. Ouyang, J. Ford, Fillia S. Makedon “Ontology-based Image Retrieval”, WSEAS MMACTEE-WAMUS-NOLASC 2003, Vouliagmeni, Athens, Greece, December 29-31, 2003

[Rahm et al 01] E. Rahm, P. Bernstein. “A survey of approaches to automatic schema matching”, VLDB Journal, 10(4):334–350, 2001.

[Maedche et al 02] A. Maedche, S. Staab, "Measuring Similarity between Ontologies", in the Proceedings of the European Conference on Knowledge Acquisition and Management EKAW-2002, Madrid, Spain, October 1-4, pp. 251-263, 2002

Satellite Image Retrieval Based On Ontology Merging

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