farah prsentatation gvip 14 juin 2008
TRANSCRIPT
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
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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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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State of the art : satellite image retrieval
• 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
03/05/23Satellite Image Retrieval Based On Ontology Merging
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State of the art : satellite image retrieval
• 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]
03/05/23Satellite Image Retrieval Based On Ontology Merging
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State of the art : satellite image retrieval
• Ontology-based approach– Define high-level concepts– Representing of image content [Ruan et al 06, Zheng et al 03]
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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Our Contribution
• Objectives
– Describe the semantic image content– Manage uncertain information– Retrieve satellite images
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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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.
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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Region Extraction
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
Region Extraction
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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Satellite image 1
Satellite image N
Region Extraction
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
Ontological Modeling
• 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
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
Sensor Ontological Model
03/05/23Satellite Image Retrieval Based On Ontology Merging
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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
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
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
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
Spatial Relation ontological Model
03/05/23Satellite Image Retrieval Based On Ontology Merging
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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
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
Ontological Model Merging
• Ontology Merging
• Approaches : – ONION, PROMPT, FCA-MERGE, Etc.
Don’t manage information uncertainty
03/05/23Satellite Image Retrieval Based On Ontology Merging
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Incompletes ontological model
Merged model
MERGING
Region Extraction
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
OWL probabilistic model
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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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
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
OWL probabilistic model
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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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
Ontological Modeling
Ontological Model Merging
Satellite images
Sensor O.M.
Scene O.M.
Spatial Relation O.M.
Semantic strategic Image Retrieval
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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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
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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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
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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Scene 1
Terrestrial zone Humid zone
MountainParcel
River
Cultivate parcelM
CP1
R
CP2
Scene 2
Terrestrial zone Humid zone
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.
03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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References
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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[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.
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[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.
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[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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03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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03/05/23Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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