Farah Prsentatation Gvip 14 Juin 2008
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Farah Prsentatation Gvip 14 Juin 2008

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Satellite Image Retrieval Based On Ontology Merging...

Satellite Image Retrieval Based On Ontology Merging
Imed Riadh Farah1, 2, Wassim Messaoudi1,2, Karim saheb ettabâa1, 2 and Basel Solaiman2
1Laboratoire de Recherche en Informatique Arabisée et Documentique Intégrée (R.I.AD.I),
Ecole Nationale des Sciences de l’Informatique. Campus Universitaire de Manouba, 2010 Manouba, Tunis, Tunisie
2ENST-Bretagne, laboratoire ITI Technopôle Brest Iroise CS 83818, 29238 Brest Cedex France

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  • Good Afternoon, I’m Wassim MESSAOUDI, an assistant professor at University of Jendouba Tunisia and member at RIADI Laboratory - ENSI. So today, I will present our work untitled Semantic strategic satellite image retrieval  
  • We starts by presenting the context of our work. Many types of sensors acquires a scene, so many types of satellite image are obtained, describing this scene. The satellite image is a representation of a scene, it is very rich by information.
  • Satellite image constitute an important information source for different domain such as urbanism management, sol occupation, spatial analysis. It’s a representation of the reality, and it need analysis and interpretation for exploiting image content. We consider a query satellite image and a base of satellite images. So, the problem addressed in this paper is How to retrieve satellite images and to improve the quality of the system retrieval.
  • Now, we present a state of the art of the satellite image retrieval In the text metadata-based approach, the images are manually annotated by text descriptors (keyword) which are then used in the retrieval process. This approach is relatively simple to implement and easy to use. But, a keyword in a document does not necessarily describe image content. It’s useful especially to a user who knows what keywords are used to index the images. To overcome the above disadvantages in text-based retrieval system, content-based image retrieval (CBIR) was introduced in the early 1980s. In this approach, images are indexed by their visual content, such as color, texture, shapes, etc. And the retrieval is based on these descriptors. This approach give improved the quality of the system retrieval However, the similarity measures between visual features do not match human perception. For example, two images can be very similar in color, size, and shape, despite containing different objects. So, semantic satellite image retrieval approach is proposed to reducing the semantic gap between visual feature and semantic object and to provide semantic in retrieval process. Several approaches are proposed such as relevant feedback, semantic template, machine learning and ontology, etc.
  • Relevant feedback approach was used in text-based information retrieval and was introduced to CBIR to bring user in the retrieval process for reducing the semantic gap between what queries represent (low-level features) and what the user thinks. In this approach, the system provides initial retrieval results. Then, the user judges the above results by selecting the accepted results. Then, a machine learning algorithm is applied to learn the user feedback [12].
  • The Machine learning approach consist of using supervised or unsupervised machine learning methods to associate low-level features with query concepts

Farah Prsentatation Gvip 14 Juin 2008 Farah Prsentatation Gvip 14 Juin 2008 Presentation 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)
    • RIADI Laboratory, ENSI, Manouba University, Tunis, Tunisia
    • 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
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • Context 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Sensor: optic, radar, infrared Satellite images Remote sensing
  • Context and problematic 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman RETRIVE ? Satellite image base
  • State of the art : satellite image retrieval
    • Text-based metadata image retrieval
    • Content-based image retrieval
    • Semantic image retrieval
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • 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
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • 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]
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • 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]
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • Our Contribution
    • Objectives
      • Describe the semantic image content
      • Manage uncertain information
      • Retrieve satellite images
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Ontological Model 1 Ontological Model 2 Ontological Model 3 Merged ontological model MODULE 1 : ONTOLOGICAL MODELMODELING AND MERGING Region Extraction Ontological Modeling Ontological Model Merging Satellite images
  • 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.
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Sensor O.M. Scene O.M. Spatial Relation O.M. Region Extraction Ontological Modeling Ontological Model Merging Satellite images Semantic strategic Image Retrieval
  • Region Extraction 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Satellite image 1 Satellite image N Sensor O.M. Scene O.M. Spatial Relation O.M. Region Extraction Ontological Modeling Ontological Model Merging Satellite images Semantic strategic Image Retrieval
  • 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
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Sensor O.M. Scene O.M. Spatial Relation O.M. Region Extraction Ontological Modeling Ontological Model Merging Satellite images Semantic strategic Image Retrieval
  • Sensor Ontological Model 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman OWL model: <owl:Class rdf:ID=&quot;Sensor&quot;/> <owl:Class rdf:ID=&quot;Active&quot;> <rdfs:subClassOf rdf:resource=&quot;#Sensor&quot;/> </owl:Class> <owl:Class rdf:ID=&quot;Passive&quot;> <rdfs:subClassOf rdf:resource=&quot;#Sensor&quot;/> </owl:Class> <owl:Class rdf:ID=&quot;Optic&quot;> <rdfs:subClassOf rdf:resource=&quot;#Passive&quot;/> </owl:Class> <owl:Class rdf:ID=&quot;Radar&quot;> <rdfs:subClassOf rdf:resource=&quot;#Active&quot;/> </owl:Class> Scene O.M. Spatial Relation O.M. Sensor Active Passive Optic Radar Region Extraction Ontological Modeling Ontological Model Merging Satellite images Sensor O.M. Semantic strategic Image Retrieval
  • Scene Ontological Model 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Sensor O.M. Spatial Relation O.M. Urban zone Scene Terrestrial zone Humid zone Mountain Communication ways Energy line Bridge Road Railway Parcel Construction Forest River Lac Sea Cultivate parcel Uncultivated parcel Canal Region Extraction Ontological Modeling Ontological Model Merging Satellite images Scene O.M. Semantic strategic Image Retrieval
  • Spatial Relation ontological Model 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Sensor O.M. Scene O.M. Relation spatiale At the right At the left Distance relation On Direction relation Postion relation Topologic relation under between Far Near Disjunction relation Inclusion relation Adjacency relation Region Extraction Ontological Modeling Ontological Model Merging Satellite images Spatial Relation O.M. Semantic strategic Image Retrieval
  • Ontological Model Merging
    • Ontology Merging
    • Approaches :
      • ONION, PROMPT, FCA-MERGE, Etc.
    • Don’t manage information uncertainty
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Sensor O.M. Scene O.M. Spatial Relation O.M. Incompletes ontological model Merged model MERGING Region Extraction Ontological Modeling Ontological Model Merging Satellite images Semantic strategic Image Retrieval
  • OWL probabilistic model 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 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 Sensor O.M. Scene O.M. Spatial Relation O.M. Region Extraction Ontological Modeling Ontological Model Merging Satellite images Semantic strategic Image Retrieval
  • OWL probabilistic model 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 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 m 3 </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 m 3 </Area> </Lake> <Urbain Zone> <Nom >ZU1</Nom> <Area> 10 Ha </Area> </Urbain Zone> + Sensor O.M. Scene O.M. Spatial Relation O.M. Region Extraction Ontological Modeling Ontological Model Merging Satellite images Semantic strategic Image Retrieval
  • 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman Merged ontological model Base of Ontological Models Similar Satellite images MODULE 2 : STRATEGIC IMAGE RETRIEVAL Similar Ontological Models Similarity Measure
  • 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
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • Example 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman 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
  • 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.
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
  • References 05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
    • [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, &quot;The Study on the Semantic Image Retrieval based on the Personalized Ontology&quot;, 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, &quot;Measuring Similarity between Ontologies&quot;, 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
    • Thank you for your attention
    05/05/10 Satellite Image Retrieval Based On Ontology Merging Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel Solaiman
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