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P1150803001

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P1150803001

  1. 1. 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
  2. 2. Outline • Context and problematic • State of the art : Satellite image retrieval • Our contribution – Ontological modeling – Ontological model merging – Satellite image Retrieval • Conclusion 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 2
  3. 3. Context and problematic 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 4 RETRIVE ? Satellite image baseSatellite image base
  4. 4. State of the art : satellite image retrieval • Text-based metadata image retrieval • Content-based image retrieval Semantic image retrieval 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 5
  5. 5. 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 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 6
  6. 6. 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] 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 7
  7. 7. 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] 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 8
  8. 8. Our Contribution • Objectives – Describe the semantic image content – Manage uncertain information – Retrieve satellite images 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 9
  9. 9. 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 10 Region Extraction Ontological Modeling Ontological Model Merging Satellite images Ontological Model 1 Ontological Model 2 Ontological Model 3 Merged ontological model MODULE1:ONTOLOGICALMODELMODELINGANDMERGING
  10. 10. 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. 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 11 Region Extraction Ontological Modeling Ontological Model Merging Satellite images Sensor O.M. Scene O.M. Spatial Relation O.M. Semantic strategic Image Retrieval
  11. 11. Region Extraction 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 12 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
  12. 12. Ontological Modeling • Ontology – Specification of a conceptualization [Gruber 1993]. Knowledge representation Extendibility and reusability A 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 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 13 Region Extraction Ontological Modeling Ontological Model Merging Satellite images Sensor O.M. Scene O.M. Spatial Relation O.M. Semantic strategic Image Retrieval
  13. 13. Sensor Ontological Model 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 14 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
  14. 14. Scene Ontological Model 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 15 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
  15. 15. Spatial Relation ontological Model 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 16 Relation spatiale At the right At the left Distance relation On Direction relation Postion relation Topologic relation underbetwee n 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
  16. 16. Ontological Model Merging • Ontology Merging • Approaches : – ONION, PROMPT, FCA-MERGE, Etc.  Don’t manage information uncertainty 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 17 Incompletes ontological model Incompletes ontological model Merged model MERGINGMERGING Region Extraction Ontological Modeling Ontological Model Merging Satellite images Sensor O.M. Scene O.M. Spatial Relation O.M. Semantic strategic Image Retrieval
  17. 17. OWL probabilistic model 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 18 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 If End 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 If End 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
  18. 18. OWL probabilistic model 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 19 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
  19. 19. 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 20 Merged ontological model Similarity Measure Base of Ontological Models Base of Ontological Models Similar Satellite images MODULE2:STRATEGICIMAGERETRIEVAL Similar Ontological Models
  20. 20. 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) ={CjA|H(Ci,Cj) v H(Cj,Ci)} : super and sub concepts of C 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 21 |))2H{L}),((( 2 2))1H{L}),((( 1 1| |))2H{L}),((( 2 2))1H{L}),((( 1 1| O2)O1,(L,TO' FSCFFSCF FSCFFSCF −− −− =  
  21. 21. Example 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 22 Scene 1 Terrestrial zone Humid zone Mountain Parcel River Cultivate parcel M CP1 R CP2 Scene 2 Terrestrial zone Humid zone Mountain Parcel Cultivate parcel M CP1 Lac L
  22. 22. 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. 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 23
  23. 23. References 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 24  [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
  24. 24. Satellite Image Retrieval Based On Ontology Merging Thank you for your attention 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 25
  25. 25. 22/07/15 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 26 Education Research Development

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