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. Outline
• Context and problematic
• State of the art : Satellite image retrieval
• Our contribution
– Ontological modeling
– Ontological model merging
– Satellite image Retrieval
• Conclusion
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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3. Context and problematic
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Satellite 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
4. State of the art : satellite image retrieval
• Text-based metadata image retrieval
• Content-based image retrieval
Semantic image retrieval
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Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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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
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Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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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]
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Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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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]
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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8. Our Contribution
• Objectives
– Describe the semantic image content
– Manage uncertain information
– Retrieve satellite images
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Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
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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
MODULE1:ONTOLOGICALMODELMODELINGANDMERGING
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.
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Satellite 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
11. Region Extraction
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Satellite 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
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
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Satellite 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
13. Sensor Ontological Model
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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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
14. Scene Ontological Model
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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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
15. Spatial Relation ontological Model
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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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. Ontological Model Merging
• Ontology Merging
• Approaches :
– ONION, PROMPT, FCA-MERGE, Etc.
Don’t manage information uncertainty
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Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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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. OWL probabilistic model
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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 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
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Satellite 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 Measure
Base of
Ontological
Models
Base of
Ontological
Models
Similar Satellite
images
MODULE2:STRATEGICIMAGERETRIEVAL
Similar Ontological
Models
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) ={CjA|H(Ci,Cj) v H(Cj,Ci)} : super and sub concepts of C
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Satellite Image Retrieval Based On Ontology Merging
Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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|))2H{L}),(((
2
2))1H{L}),(((
1
1|
|))2H{L}),(((
2
2))1H{L}),(((
1
1|
O2)O1,(L,TO'
FSCFFSCF
FSCFFSCF
−−
−−
=
21. Example
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Satellite 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
Mountain
Parcel
River
Cultivate parcel
M
CP1
R
CP2
Scene 2
Terrestrial zone Humid zone
Mountain
Parcel
Cultivate parcel
M
CP1
Lac
L
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.
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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23. 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
24. Satellite Image Retrieval Based
On Ontology Merging
Thank you for your attention
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Imed Riadh farah,Wassim Messaoudi, Karim saheb ettabâa and Basel
Solaiman
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Education Research
Development
Editor's Notes
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
