1. MMSEM background Dr Ioannis Pratikakis Institute of Informatics & Telecommunications NCSR “Demokritos”, Athens, Greece MMSEM – F2F meeting Amsterdam, 10 July 2006

2. MMSEM – F2F meeting, Amsterdam, 10/07/20062 NCSR “Demokritos” - Athens, GREECE The largest self-governing research organisation, under the supervision of the Greek Government It is composed of the following Institutes: Biology Biology Materials Science Materials Science Microelectronics Microelectronics Informatics & Telecommunications Informatics & Telecommunications Nuclear Technology & Radiation Protection Nuclear Technology & Radiation Protection Nuclear Physics Nuclear Physics Radioisotopes & Radiodiagnostic Producrs Radioisotopes & Radiodiagnostic Producrs Physical Chemistry Physical Chemistry

3. MMSEM – F2F meeting, Amsterdam, 10/07/20063 Institute of Informatics and Telecommunications (IIT) CIL Computational Intelligence Laboratory SKEL Software & Knowledge Engineering Laboratory Informatics Section

4. MMSEM – F2F meeting, Amsterdam, 10/07/20064 SKEL profile Information Integration User-friendly information access Ontology Creation and Maintenance SKEL researchers aim to develop knowledge technologies that will enable the efficient, cost-effective and user-adaptive management and presentation of information

5. MMSEM – F2F meeting, Amsterdam, 10/07/20065 Basic Research Grammar induction Active learning of classifiers Focused crawling Wrapper induction Information extraction Natural language generation Evolving summarization Ontology population and enrichment Web usage mining

6. MMSEM – F2F meeting, Amsterdam, 10/07/20066 Applied Research –The general-purpose language engineering platform Ellogon (http://www.ellogon.org/)http://www.ellogon.org/ –Language processing tools and resources –The i-DIP platform for developing web content collection and extraction systems –The QUATRO proxy server, for validating RDF labels of web resources –The FILTRON e-mail filter, that blocks unsolicited commercial e-mail (spam messages) –The FilterX Web proxy filter, that blocks obscene Web content –Tools for creating and maintaining ontologies –The PServer general-purpose server for personalization –The KOINOTHTES system for knowledge discovery from web usage data –An authoring tool for porting language generation systems to new domains and languages

7. MMSEM – F2F meeting, Amsterdam, 10/07/20067 CIL profile Neural Networks Computational Intelligence- Pattern recognition background Biologically inspired modelling Bayesian networks Support Vector Machines Multimedia Information Processing, Semantic analysis & Retrieval Image Video 3D Graphics Multimedia Semantic Model

8. MMSEM – F2F meeting, Amsterdam, 10/07/20068 Preprocessing and feature extraction methods Machine learning (neural networks, statistical, support vector machines) Novel algorithm development and testing Biologically inspired algorithms and architectures CIL: Platform for intelligent information processing

9. MMSEM – F2F meeting, Amsterdam, 10/07/20069 CIL: Processing and Recognition of old manuscripts Feature extraction Recognition

10. MMSEM – F2F meeting, Amsterdam, 10/07/200610 Camera Based Document Analysis & Recognition Text Identification in Web images Page Segmentation Table Detection

11. CIL: Word spotting-Image based search in early handwritten and printed documents

12. MMSEM – F2F meeting, Amsterdam, 10/07/200612 Query view Results and relative similarity to the query CIL: Content Based Image Retrieval

13. MMSEM – F2F meeting, Amsterdam, 10/07/200613 CIL: 3-D Graphics retrieval based on shape Query 3D Model First 12 answers

14. MMSEM – F2F meeting, Amsterdam, 10/07/200614 CIL: Human Tracking Tracker initialisation through –Face detection –Separation from background –Motion field calculation Tracking methods –CAMSHIFT –Snakes Features to use for tracking: –Skin color –Clothing color - texture

15. MMSEM – F2F meeting, Amsterdam, 10/07/200615 CIL: Human Behavior Analysis Behavior modeling using –Bayesian Networks –Hidden Markov Models Application case: Violence detection in video Automatic violence detection:

16. BOEMIE Bootstrapping Ontology Evolution with Multimedia Information Extraction Dr Ioannis Pratikakis Institute of Informatics & Telecommunications NCSR “Demokritos”, Athens, Greece MMSEM – F2F meeting Amsterdam, 10 July 2006

17. MMSEM – F2F meeting, Amsterdam, 10/07/200617 Contents Consortium Motivation BOEMIE proposal Application scenario Concluding remarks

18. MMSEM – F2F meeting, Amsterdam, 10/07/200618 BOEMIE project Bootstrapping Ontology Evolution with Multimedia Information Extraction STRP, IST-2004-2.4.7 “Semantic-based Knowledge and Content Systems” –Started: 01/03/2006, Duration: 36 months Consortium –Inst. of Informatics & Telecommunications, NCSR “Demokritos” (SKEL & CIL), Greece (Coordinator) –Fraunhofer Institute for Media Communication (NetMedia), Germany –Dip. di Informatica e Comunicazione, University of Milano (ISLab), Italy –Inst. of Telematics and Informatics CERTH (IPL), Greece –Hamburg University of Technology (STS), Germany –Tele Atlas, Belgium

19. MMSEM – F2F meeting, Amsterdam, 10/07/200619 Multimedia Content Analysis - I Multimedia content grows with increasing rates Hard to provide semantic indexing of multimedia content Significant advances in automatic extraction of low-level features from visual content Little progress in the identification of high- level semantic features

20. MMSEM – F2F meeting, Amsterdam, 10/07/200620 Multimedia Content Analysis - II Inadequate the analysis of single modalities Little progress in the effective combination of semantic features from different modalities. Significant effort in producing ontologies for semantic webs. Hard to build and maintain domain- specific multimedia ontologies.

21. MMSEM – F2F meeting, Amsterdam, 10/07/200621 Existing approaches - I Combination of modalities may serve as a verification method, a method compensating for inaccuracies, or as an additional information source Combination methods may be iterated allowing for incremental use of context Major open issues in combination concern –the efficient utilization of prior knowledge, –the specification of open architecture for the integration of information from multiple sources, and –the use of inference tools

22. MMSEM – F2F meeting, Amsterdam, 10/07/200622 Existing approaches - II Most of the extraction approaches are based on machine learning methods With the advent of promising methodologies in multimedia ontology engineering –knowledge-based approaches are expected to gain in popularity and –be combined with the machine learning methods

23. MMSEM – F2F meeting, Amsterdam, 10/07/200623 Existing approaches – III Use of Ontologies to “drive” the information extraction process –providing high-level semantic information that helps disambiguating the labels assigned to MM objects Major open issues in building and maintaining MM ontologies concern –automatic mapping between low level audio-visual features and high level domain concepts, –automated population and enrichment from unconstrained content, –employing of ontology coordination techniques when multiple ontologies are present

24. MMSEM – F2F meeting, Amsterdam, 10/07/200624 Existing approaches - IV Synergy between information extraction and ontology learning through a bootstrapping process –to improve both the conceptual model and the extraction system through iterative refinement Applied so far in knowledge acquisition from textual content –bootstrapping starts with an information extraction system that uses a domain ontology, or –bootstrapping starts with a seed ontology, usually small

25. MMSEM – F2F meeting, Amsterdam, 10/07/200625 BOEMIE proposal - I Driven by domain-specific multimedia ontologies, BOEMIE systems will be able to identify high-level semantic features in image, video, audio and text and fuse these features for optimal extraction. The ontologies will be continuously populated and enriched using the extracted semantic content. This is a bootstrapping process, since the enriched ontologies will in turn be used to drive the multimedia information extraction system.

26. MMSEM – F2F meeting, Amsterdam, 10/07/200626 BOEMIE Proposal - II BOEMIE Proposal - II EVOLVED ONTOLOGY INITIAL ONTOLOGY POPULATION & ENRICHMENT COORDINATION INTERMEDIATE ONTOLOGY ONTOLOGY EVOLUTION TOOLKIT LEARNING TOLS REASONING ENGINE MATCHING TOOLS ONTOLOGY MANAGEMENT TOOL ONTOLOGY INITIALIZATION AND CONTENT MANAGEMENT TOOL ONTOLOGY EVOLUTION EVENTS DATABASE MAPS DATABASE MAP ANNOTATION INTERFACE SEMANTICS EXTRACTION RESULTS OTHER ONTOLOGIES SEMANTICS EXTRACTION MULTIMEDIA CONTENT SEMANTICS EXTRACTION TOOLKIT TEXT EXTRACTION TOOLS AUDIO EXTRACTION TOOLS INFORMATION FUSION TOOLS VISUAL EXTRACTION TOOLS FROM VISUAL CONTENT FROM NON-VISUAL CONTENT FROM FUSED CONTENT Content Collection (crawlers, spiders, etc.)

27. MMSEM – F2F meeting, Amsterdam, 10/07/200627 BOEMIE proposal - III Semantics extraction –Emphasis to visual content, from images and video, due to its richness and the difficulty of extracting useful information. –Non-visual content, audio/speech and text, will provide supportive evidence, to improve extraction precision. –Fusing information from multiple media sources is needed since no single modality is powerful enough to encompass all aspects of the content and identify concepts precisely.

28. MMSEM – F2F meeting, Amsterdam, 10/07/200628 BOEMIE proposal - IV Multimedia Semantic Model –development of a unifying representation, a “multimedia semantic model” to integrate: a multimedia ontology which –describes the structure of multimedia content (content objects, such as a segment in a static image, a time window in audio, a video shot,...), –describes visual characteristics of content objects in terms of low-level features (colour, shape, texture, motion, …) a domain ontology which contains knowledge about the selected application domain, and a geographic ontology which contains additional knowledge about the locations to be used

29. MMSEM – F2F meeting, Amsterdam, 10/07/200629 BOEMIE proposal – V Ontology evolution involvesOntology evolution –ontology population and enrichment, i.e., addition of concepts, relations, properties and instances, –coordination of homogeneous ontologies e.g. when more than one ontology for the same domain are available, and heterogeneous ontologies, e.g., updating the links between a modified domain ontology and a multimedia descriptor ontology, –maintenance of semantic consistency any of the above changes may generate inconsistencies in other parts of the same ontology, in the linked ontologies or in the annotated content base.

30. MMSEM – F2F meeting, Amsterdam, 10/07/200630 Application scenario - I Enrichment of digital maps with semantic information –Domain: sport events in a given area (big cities) Sub-domain initially selected: athletics (running, jumping and throwing events) Cities will be selected taking into account: number and frequency of sports events, availability of multimedia coverage in English of these events, availability of map and landmark data for the city –BOEMIE will collect multimedia coverage for sport events and strive to extract as much knowledge from the extracted features as possible, using and evolving the corresponding domain ontologies –The identified entities and their properties, will be linked to geographical locations and stored in a content server –The user will be provided with immediate access to the annotated content

31. MMSEM – F2F meeting, Amsterdam, 10/07/200631 Application scenario - II Querying –The prototype will perform reasoning using knowledge from the domain ontology and geographical knowledge to deduce further information and answer user queries. –The user will be able to perform the following queries: events in a time frame events of a particular type events at a certain location persons related to events events similar to a given one events at nearby venues points of interest near a venue combinations of the above

32. MMSEM – F2F meeting, Amsterdam, 10/07/200632 Application scenario - II Querying: an example –Find out the location of the venues in which Athlete A has participated in a high jump competition in the city X. From transcribed radio commentary, the BOEMIE system knows that in 2001, the World Championships in Athletics were held in city X in venue Y. From the geographical data, it knows the exact location of venue Y in city X. It has further analyzed a video snippet and identified it as a high jump event. From the meta data of the video, the system knows its date of recording in 2001, and in the audio of this snippet, the keywords “X” and A's name were spotted. Therefore, the system can deduce that A has indeed participated in a high jump competition in city X, namely the World Championships in Athletics 2001. As a result, the BOEMIE system presents all used multimedia assets as “prove” for its answer and gives the exact location of the venue where the World Championship in Athletics took place.

33. MMSEM – F2F meeting, Amsterdam, 10/07/200633 Concluding remarks - I BOEMIE work aims to initiate a discussion on the problem of knowledge acquisition and the synergy of information extraction and ontology evolution Several open issues: –the role of ontology in fusing information from multiple media –ways to learn the optimal combination of features derived from MM content –how existing ontology languages can be extended to tackle the requirements of MM content analysis –the application of existing ontology learning and inference techniques in the context of MM content –the application of the coordination task in a new context which involves not only homogeneous ontologies, but also heterogeneous ones

34. MMSEM – F2F meeting, Amsterdam, 10/07/200634 Concluding remarks - II The main measurable objective of BOEMIE initiative is to improve significantly the performance of existing single-modality approaches in terms of scalability and precision. Towards that goal, our aim is to –develop a new methodology for extraction and evolution, using a rich multimedia semantic model, and –realize it as an open architecture that will be coupled with the appropriate set of tools.

35. MMSEM – F2F meeting, Amsterdam, 10/07/200635 BOEMIE Bootstrapping Ontology Evolution with Multimedia Information Extraction http://www.boemie.org http://www.boemie.org THANK YOU !!!