Yiannis Kompatsiaris Multimedia Knowledge Laboratory CERTH - Informatics and Telematics Institute
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1 Convergence of multimedia and knowledge technologies acemedia, BOEMIE, MESH, X-Media, K-Space, VITALAS and VICTORY Practitioner Day CIVR 2007 Yiannis Kompatsiaris CERTH -
2 Introduction Content - applications A common view Multimedia Ontologies Analysis Reasoning Retrieval Common issues Dissemination Conclusions Outline 2 2
3 TITLE DIRECTO R TAKE SCENE Multimedia Content Content adaptation and distribution - Multiple Terminal & Networks Networks Storage & Devices Segmentation KA Analysis Labeling Web 2.0 photo - video applications Cross-media analysis Context Reasoning Metadata Generation & Representation Hybrid / Content-based retrieval recommendations and personalization Semantic technology in Markets 3 3
4 Need for annotation + metadata The value of information depends on how easily it can be found, retrieved, accessed, filtered or managed in an active, personalized way 4 4
5 Content - Applications Content Knowledge Extraction Applications Personal Sports Semantic Desktop Retrieval Industrial Fashion News 3D News Commercial Personalization Mobile 5 5
6 Content - Applications Content Knowledge Extraction Applications Personal Retrieval Commercial Personalization Mobile 6 6
7 Content - Applications Content distribution and adaptation Sharing ACE concept Actionable content 7 7
8 Content - Applications Content Knowledge Extraction Applications Retrieval News Personalization Mobile 8 8
9 Content - Applications News Syndication Multi-National & Local news providers 9 9
10 Content - Applications Content Knowledge Extraction Applications Semantic Desktop Retrieval Industrial 10 10
11 Content - Applications Large-Scale content Process support Industry content 11 11
12 Content - Applications Content Knowledge Extraction Applications Retrieval 3D 12 12
13 Content - Applications From raw data to semantics Maximise automation of the shape knowledge lifecycle From semantics to model raw data geometric model structural model Extracting semantic content Embedding semantic content conceptual sketch semantically structured model shape facets and semantic mapping semantic model geometric model 13 13
14 Content - Applications Content Knowledge Extraction Applications Sports Retrieval 14 14
15 Content - Applications OTHER ONTOLOGIES EVENTS DATABASE SEMANTICS EXTRACTION FROM VISUAL CONTENT MAP ANNOTATION INTERFACE FROM NON-VISUAL CONTENT MAPS DATABASE Content Collection (crawlers, spiders, etc.) INITIAL ONTOLOGY SEMANTICS EXTRACTION RESULTS FROM FUSED CONTENT ONTOLOGY EVOLUTION MULTIMEDIA CONTENT EVOLVED ONTOLOGY Automatic semantic annotation of digital maps POPULATION & ENRICHMENT COORDINATION INTERMEDIATE ONTOLOGY 15 15
16 Content - Applications Content Knowledge Extraction Applications Personal Sports Retrieval News 16 16
17 Content - Applications Integration K-Space R&D Fellowships Dissemination Network of Excellence Emphasis on integration of research activities 17 17
18 Content - Applications Content Knowledge Extraction Applications Retrieval Fashion News Large-Scale Real use cases News Personalization 18 18
19 Content - Applications Content Knowledge Extraction Applications Retrieval 3D P2P and Mobile Mobile 19 19
20 Knowledge Extraction A common view Single Modality Analysis Additional Analysis Information Semantic Analysis Manual Annotation - Models Knowledge Infrastructure (Multimedia Ontology) Implicit Knowledge Signal Level Explicit Knowledge Logic - Semantics & Hybrid Level 20 20
21 Feature extraction Text, Image analysis Segmentation, SVMs Evidence generation Vehicle, Single Building Modality Analysis Knowledge Extraction Single Modality Analysis A common view Classifiers fusion Global vs. Local Modalities fusion Additional Context Ambulance Analysis Information Additional Analysis Information Reasoning Fusion of annotations Consistency checking Higher-level concepts/events Emergency Semantic scene Analysis Semantic Analysis Manual Annotation - ManualModels Multimedia Annotation content annotation - Models tools Training (Statistical) Modeling Knowledge Infrastructure (Multimedia Ontology) Knowledge Infrastructure (Multimedia Domain Ontology) Multimedia content Annotations Algorithms - Features Context 21 21
22 Use of ontologies Metadata representation Annotation Interoperability Reasoning Extracting higher-level annotations Consistency checking Fusion Ontology-driven analysis Retrieval Personalization 22 22
23 Multimedia Ontologies Multimedia content structure acemedia(mpeg-7, RDF), content) Multimodality MESH(OWL), BOEMIE (OWL-DL) K-Space, X-Media (COMM, OWL, DOLCE) Fuzziness K-Space, X-Media (Fuzzy-OWL) Changing knowledge BOEMIE (evolution) X-Media (versioning, reasons of change) Specific domains 23 23
24 COMM: Core Ontology of MultiMedia K-Space, X-Media Instead of translating MPEG-7 1-to-1 into an ontology, COMM provides 5 multimedia design patterns which formalize basic notions of multimedia annotation Digital data pattern Decomposition pattern Content annotation pattern Media annotation pattern Semantic annotation pattern Usage of DOLCE as modeling basis and consideration of common requirements for multimedia annotation COMM already covers large parts of MPEG-7 (some additional patterns may be required for complete coverage) DOLCE DigitalData MultimediaData DOLCE Description StructuredDataDescription MPEG 7Descriptor Decomposition pattern OutputRole ProcessingRole OutputSegmentRole plays DigitalData MultimediaData defines Method Algorithm InputRole InputSegmentRole plays AnnotationRole satisfies Situation SegmentDecomposition SegmentationAlgorithm Content annotation pattern setting defines ProcessingRole OutputRole Method Algorithm InputRole AnnotatedDataRole satisfies plays Situation Annotation setting setting plays 24 24
25 Multimedia Information Objects MESH Example: Analysis of a Multimedia Web Document Linguistic-IO / Visual-IO Format Format: JPG, UTF-8 Lang: DE orderedby instanceof realizedby MultimediaFile text, image 1 2 Segmentation-Tool interpretedby hasdecomposition about MatchTeam Decomposition spatio-temporal-region about TextSegment 1 2 hassegment hascontent Image Text hascontent hassegment 25 25
26 Multimedia Content Annotation M-Ontomat-Annotizer (acemedia, K-Space) 26 26
27 Multimedia Content Analysis MPEG-7 widely used for LL features Segmentation and feature extraction tools (acemedia, K-Space) Well-known classifiers applied and developed SVMs, EM, HMM Bio-inspired approaches Increasing use of context (acemedia, K-Space) Spatial, Frequency, EXIF Fusion Classifiers (K-Space, MESH: global+local) Modalities X-Media (Text+Image+1D data) MESH (Text+Speech+Video) acemedia (Text+Video) Mostly statistical and machine learning (implicit) based but also Hybrid (implicit + explicit, K-Space) 27 27
28 Context and Reasoning for Analysis acemedia sky <RDF /> sea person/bear KAA rock rock/beach sea, sky beach scene classification beach/rock beach scene person face person/face detection other analysis methods Creation of contextual information multimedia reasoning Use of contextual information From metadata layer spatio-temporal relations Domain knowledge Reduction of label sets Merging of segments 28 28
29 Classification Results acemedia Segment s hypothesis set Natural-Person: Sailing-Boat: Sand: Building: Pavement: Road: Body-Of-Water: Cliff: Cloud: Mountain: Sea: Sky: Stone: Waterfall: Wave: Dried-Plant: Dried-Plant-Snowed: Foliage: Grass: Tree: Trunk: Snow: Sunset: Car: Ground: Lamp-Post: Statue:
30 Semantic Region Merging K-Space RSST Sky Building Semantic RSST Ground Sea 30 30
31 Cross Media Knowledge Acquisition X-Media Cross Media approaches: Result level: combining results obtained from different systems on different types of media Extractor level: using system results from different types of media as annotation or background knowledge Feature level: using features coming from different media. Cross Media Framework: Multimedia Document Processing: Extract single & cross media features Feature Processing: Find optimal representation of feature space Cross media data models Create knowledge models for all concepts Cross media dependency models Integrate background knowledge & exploit causality information 31 31
32 Reasoning Support of imprecision - uncertainty Logic-based approaches Extensions of formal theories (X-Media, K-Space) Ad-hoc solutions based on crisp reasoners (acemedia) Statistical approaches (X-Media) Used for: Fusion Consistency checking Higher-level results 32
33 Ad-hoc Fuzzy Reasoning acemedia 1. Classical (crisp) DL reasoning applied on assertions, leaving out fuzzy degrees image1 region3 region1 2. Axioms revisited by external module to update appropriately the degrees according to fuzzy interpretation semantics: region2 Remarks Annotations (fuzzy ABox) considered: fuzzy, positive, concept assertions crisp role assertions Prior knowledge (TBox) considered: Crisp inclusion and equivalence axioms 33 33
34 Fuzzy OWL K-Space, X-Media Automatically Derived Multimedia Annotation is often imprecise or errorprone Model this uncertainty Extend OWL with fuzzy A-Box region4 shows an object which is a ball with a fuzzy degree of 0.8 and a pumpkin with a fuzzy degree of
35 Abduction as non-standard retrieval: Acquire what should be added to a knowledge base (Σ,Δ) to make a query (set of assertions) Γ true TBox ABox Interpretation as abduction: let Γ be the analysis produced concept and role assertions, Γ1 : by default assertions, Γ2 : ones that need to be explained Σ includes apart from DL axioms, rules to answer the Γ queries in a backward-chaining way. Multiple explanations may be obtained. Consistency checking eliminates not valid answers Preference measure according to the number of new assertions required ΒΟΕΜΙΕ Σ Possible explanations Δ 1 : adds 2 new individuals and infers Pole_Vault Δ 2 : adds 1 new individual and infers Pole_Vault Γ 2 Δ 3 : adds 1 new individual and infers High_Jump (neglects though pole 1 :Pole) 35 35
36 VITALAS: Retrieval VITALAS, Victory Adapting the search space to the user profile and providing interactive functionalities to control the results The system validation will be performed on professional collections, up to 10,000 hours of video (television archives INA/IRT) and still images (Belga) The textual annotation would have different interpretation regarding the usage context. VICTORY: 3D and multimedia distributed content (MultiPedia) into Peer-to- Peer (P2P) and mobile Ρ2Ρ networks 3D content (and context) analysis, personalization, 3D object watermarking techniques 36 36
37 acemedia applications Standalone acemedia PC applications Web-based 37 37
38 Common (Open) Issues Evaluation Annotated content Ontologies Fusion in analysis Uncertainty in reasoning Large-Scale Generic vs. Specific approaches Multiple domains support 38 38
39 Dissemination Activities SMART: Semantic MultimediA Research and Technology, networking cluster SAMT: International Conference on Semantics and digital Media Technologies (EWIMT) 2007: 5-7 December 2007, Genova, Italy SSMS: Summer School on Multimedia Semantics 2007: Glasgow, UK, July 15-21, 2007 Special issues, sessions, workshops, books 39 39
40 Conclusions Semantic analysis of multimedia is already providing results Fundamental and applied research in Logic-based + signal approaches Implicit + explicit (knowledge) approaches Different applications and requirements Ongoing research in all areas Future direction: analysis+reasoning for social (Web 2.0) applications 40 40
41 Many thanks to the projects! 41 41
42 Thank you! CERTH-ITI /
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