1 / 35

MMSEM background

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

marcel
Download Presentation

MMSEM background

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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 • Materials Science • Microelectronics • Informatics & Telecommunications • Nuclear Technology & Radiation Protection • Nuclear Physics • Radioisotopes & Radiodiagnostic Producrs • Physical Chemistry MMSEM – F2F meeting, Amsterdam, 10/07/2006

  3. Institute of Informatics and Telecommunications (IIT) Informatics Section SKEL Software & Knowledge Engineering Laboratory CIL Computational Intelligence Laboratory MMSEM – F2F meeting, Amsterdam, 10/07/2006

  4. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  5. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  6. Applied Research • The general-purpose language engineering platform Ellogon (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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  7. Multimedia Semantic Model CIL profile Biologically inspired modelling Neural Networks Computational Intelligence- Pattern recognition background Multimedia Information Processing, Semantic analysis & Retrieval Bayesian networks Support Vector Machines Image 3D Graphics Video MMSEM – F2F meeting, Amsterdam, 10/07/2006

  8. CIL: Platform for intelligent information processing • Preprocessing and feature extraction methods • Machine learning (neural networks, statistical, support vector machines) • Novel algorithm development and testing • Biologically inspired algorithms and architectures MMSEM – F2F meeting, Amsterdam, 10/07/2006

  9. CIL: Processing and Recognition of old manuscripts Recognition Feature extraction MMSEM – F2F meeting, Amsterdam, 10/07/2006

  10. Page Segmentation Camera Based Document Analysis & Recognition Text Identification in Web images Table Detection MMSEM – F2F meeting, Amsterdam, 10/07/2006

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

  12. CIL: Content Based Image Retrieval Query view Results and relative similarity to the query MMSEM – F2F meeting, Amsterdam, 10/07/2006

  13. CIL: 3-D Graphics retrieval based on shape Query 3D Model First 12 answers MMSEM – F2F meeting, Amsterdam, 10/07/2006

  14. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  15. CIL: Human Behavior Analysis • Behavior modeling using • Bayesian Networks • Hidden Markov Models • Application case: Violence detection in video Automatic violence detection: MMSEM – F2F meeting, Amsterdam, 10/07/2006

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

  17. Contents • Consortium • Motivation • BOEMIE proposal • Application scenario • Concluding remarks MMSEM – F2F meeting, Amsterdam, 10/07/2006

  18. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  19. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  20. 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. MMSEM – F2F meeting, Amsterdam, 10/07/2006

  21. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  22. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  23. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  24. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  25. 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. MMSEM – F2F meeting, Amsterdam, 10/07/2006

  26. MAP ANNOTATION INTERFACE ONTOLOGY EVOLUTION TOOLKIT Content Collection (crawlers, spiders, etc.) EVENTS DATABASE MAPS DATABASE OTHER ONTOLOGIES ONTOLOGY MANAGEMENT TOOL LEARNING TOLS SEMANTICS EXTRACTION REASONING ENGINE FROM VISUAL CONTENT FROM NON-VISUAL CONTENT MATCHING TOOLS FROM FUSED CONTENT MULTIMEDIA CONTENT SEMANTICS EXTRACTION RESULTS INITIAL ONTOLOGY ONTOLOGY EVOLUTION EVOLVED ONTOLOGY COORDINATION POPULATION & ENRICHMENT INTERMEDIATE ONTOLOGY SEMANTICS EXTRACTION TOOLKIT VISUAL EXTRACTION TOOLS TEXT EXTRACTION TOOLS AUDIO EXTRACTION TOOLS ONTOLOGY INITIALIZATION AND CONTENT MANAGEMENT TOOL INFORMATION FUSION TOOLS BOEMIE Proposal - II MMSEM – F2F meeting, Amsterdam, 10/07/2006

  27. 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. MMSEM – F2F meeting, Amsterdam, 10/07/2006

  28. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  29. BOEMIE proposal – V • Ontology evolution involves • 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. MMSEM – F2F meeting, Amsterdam, 10/07/2006

  30. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  31. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  32. 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. MMSEM – F2F meeting, Amsterdam, 10/07/2006

  33. 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 MMSEM – F2F meeting, Amsterdam, 10/07/2006

  34. 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. MMSEM – F2F meeting, Amsterdam, 10/07/2006

  35. BOEMIE Bootstrapping Ontology Evolution with Multimedia Information Extractionhttp://www.boemie.org THANK YOU !!! MMSEM – F2F meeting, Amsterdam, 10/07/2006

More Related