Presentation on theme: "Office of SA to CNS GeoIntelligence 2009. Introduction Data Mining vs Image Mining Image Mining - Issues and Challenges CBIR Image Mining Process Ontology."— Presentation transcript:
Introduction Data Mining vs Image Mining Image Mining - Issues and Challenges CBIR Image Mining Process Ontology for Image Mining Conclusion GeoIntelligence 2009 Office of SA to CNS An Overview of Image Mining
Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain Military Applications: Mobility Analysis Traffficability Analysis Potential Corridor of Landing Image Mining GeoIntelligence 2009 Office of SA to CNS
GeoIntelligence 2009 The features may include: Color (in various channels), Texture (e.g. Directionality, likeliness, contrast, roughness and coarseness), edge, luminance, shape, spatial relations, temporal information, statistical measures (e.g. moments – mean, variance, standard deviation, skewness etc).
Data mining searches: Valid patterns Previously unknown patterns Potentially useful patterns Understandable patterns Image mining extracts: Strategic information Relationships and patterns Landscape aspects Challenges (image mining) Relative values Spatial information Multiple interpretation Patterns representation GeoIntelligence 2009 Office of SA to CNS
Image information mining is an interdisciplinary endeavor Computer vision (image processing) Pattern recognition (classification & clustering) Databases (images & ancillary data) Information Retrieval (indexing and queries) Challenges of mining information in remote sensing images Multi /hyper spectral (huge size, different formats) Variability of data sets (formats, types and structures) Time consuming preprocessing (correction and registration) Complex spatial / temporal associations Feature extraction & semantic definition (application specific) Ancillary data (climate variables, digital elevation model) Interpretation (a-priori and domain knowledge) GeoIntelligence 2009 Office of SA to CNS
GeoIntelligence 2009 Office of SA to CNS Content-based image retrieval (CBIR) Modeling the contents of the image as a set of attributes Using an integrated feature-extraction/object-recognition system
Image Mining Process Image Database Pre-processing Transformation & Feature Extraction Mining Interpretation & Evaluation Knowledge Office of SA to CNS GeoIntelligence 2009
Graph Mining Approach Attribute Relational Graph (ARG) Regional Adjacency Graph (RAG) Ontological Approach Image Mining Approaches GeoIntelligence 2009 Office of SA to CNS
Method Ontology Structural Ontology Physical Ontology Semantic Mediator Application Ontology Task Ontology Ontology for Image Mining Incorporation of semantic information into the knowledge discovery process Ontology describes a particular reality with a specific vocabulary, using a set of hypothesis related to the intentional meaning of the words in this vocabulary Physical ontology Structural ontology Method Ontology Office of SA to CNS GeoIntelligence 2009
Image Mining Systems GeoMiner A spatial data mining system developed by Han et al (1997) ADaM A NASA-developed Image Mining System MSIM A Multi-sensor Image Mining System developed by BAE Systems GeoIntelligence 2009 Office of SA to CNS
Conclusion C urrently, most image processing techniques are designed to operate on a single image V ery few techniques for image data mining and information extraction in large image data sets “ K nowledge gap” in the process of deriving information from images and digital maps F uture research directions in remote sensing image mining include tracking individual trajectories of change GeoIntelligence 2009 Office of SA to CNS
QUESTIONS?QUESTIONS? GeoIntelligence 2009 Office of SA to CNS
Satellite Data TERRAIN DATABASE MANAGEMENT SYSTEM (TDMS) TDSS Terrain Decision Support System Attribute Data, Spatial Data, & Knowledge Base Spatial DB Server (GIS) R DB Server (RDBMS) Relational DB Miner Spatial/Image DB Miner TKDD Terrain Knowledge Discovery from Data Format Converter TD&CS VLDH Very Large Database Handler Military Applications Interactive Mining I/F Application Interface DTDB Terrain Analysis and Visualization Training Set, Testing & Validation Data Set MGD TRMS TPMS Map Data DATA INPUT Expert Refinement Discovered Rules/ Features (Natural & Manmade) ADS Application Development System Knowledge Acquisition I/F Domain Expert Knowledge Base Knowledge compiler GIS Mapping I/F Inference Mechanism PCLNMOCCM Field Data Scale Converter Projection Converter DTS Data Transformation System GIS Mapping I/F DTRL CCM-Cross Country Mobility NMO-Natural & Manmade Obstacles PCL-Potential Corridor of Landing TD&CS-Troops Deployment & Camping Sites LOS-Line of Sight LOS
Your consent to our cookies if you continue to use this website.