Prof. Thomas Sikora Technische Universität Berlin Communication Systems Group Thursday, 2 April 2009 Integration Activities in “Tools for Tag Generation“

Slides:



Advertisements
Similar presentations
Data Mining and the Web Susan Dumais Microsoft Research KDD97 Panel - Aug 17, 1997.
Advertisements

GMD German National Research Center for Information Technology Darmstadt University of Technology Perspectives and Priorities for Digital Libraries Research.
A Human-Centered Computing Framework to Enable Personalized News Video Recommendation (Oh Jun-hyuk)
Pseudo-Relevance Feedback For Multimedia Retrieval By Rong Yan, Alexander G. and Rong Jin Mwangi S. Kariuki
Automatic Video Shot Detection from MPEG Bit Stream Jianping Fan Department of Computer Science University of North Carolina at Charlotte Charlotte, NC.
Automated Shot Boundary Detection in VIRS DJ Park Computer Science Department The University of Iowa.
Taxonomic classification for web- based videos Author: Yang Song et al. (Google) Presenters: Phuc Bui & Rahul Dhamecha.
Toward Automatic Music Audio Summary Generation from Signal Analysis Seminar „Communications Engineering“ 11. December 2007 Patricia Signé.
Image Information Retrieval Shaw-Ming Yang IST 497E 12/05/02.
Using Multiple Synchronized Views Heymo Kou.  What is the two main technologies applied for efficient video browsing? (one for audio, one for visual.
Digital Video Archiving. ViArchive Overview ViArchive provides user friendly solutions for… – uploading video clips with metadata (searchable file info.
Personalized Abstraction of Broadcasted American Football Video by Highlight Selection Noboru Babaguchi (Professor at Osaka Univ.) Yoshihiko Kawai and.
1 Texmex – November 15 th, 2005 Strategy for the future Global goal “Understand” (= structure…) TV and other MM documents Prepare these documents for applications.
Discussion on Video Analysis and Extraction, MPEG-4 and MPEG-7 Encoding and Decoding in Java, Java 3D, or OpenGL Presented by: Emmanuel Velasco City College.
Content-based Video Indexing, Classification & Retrieval Presented by HOI, Chu Hong Nov. 27, 2002.
 Hamed Sadeghi Neshat.  With Internet delivery of video content surging to an unprecedented level, video advertising is becoming increasingly pervasive.
Chapter 11 Beyond Bag of Words. Question Answering n Providing answers instead of ranked lists of documents n Older QA systems generated answers n Current.
ADVISE: Advanced Digital Video Information Segmentation Engine
Supervised by Prof. LYU, Rung Tsong Michael Department of Computer Science & Engineering The Chinese University of Hong Kong Prepared by: Chan Pik Wah,
Information Retrieval Concerned with the: Representation of Storage of Organization of, and Access to Information items.
Multimedia Search and Retrieval Presented by: Reza Aghaee For Multimedia Course(CMPT820) Simon Fraser University March.2005 Shih-Fu Chang, Qian Huang,
T.Sharon 1 Internet Resources Discovery (IRD) Video IR.
Presentation Outline  Project Aims  Introduction of Digital Video Library  Introduction of Our Work  Considerations and Approach  Design and Implementation.
Re-ranking Documents Segments To Improve Access To Relevant Content in Information Retrieval Gary Madden Applied Computational Linguistics Dublin City.
Department of Computer Science and Engineering, CUHK 1 Final Year Project 2003/2004 LYU0302 PVCAIS – Personal Video Conference Archives Indexing System.
Visual Information Retrieval Chapter 1 Introduction Alberto Del Bimbo Dipartimento di Sistemi e Informatica Universita di Firenze Firenze, Italy.
Presented by Zeehasham Rasheed
Video Search Engines and Content-Based Retrieval Steven C.H. Hoi CUHK, CSE 18-Sept, 2006.
1 Final Year Project 2003/2004 LYU0302 PVCAIS – Personal Video Conference Archives Indexing System Supervisor: Prof Michael Lyu Presented by: Lewis Ng,
Multimodal Analysis Video Representation Video Highlights Extraction Video Browsing Video Retrieval Video Summarization.
김덕주 (Duck Ju Kim). Problems What is the objective of content-based video analysis? Why supervised identification has limitation? Why should use integrated.
CADAL Digital Library Wu Jiang-Qin,Zhuang Yue-Ting Pan Yun-he College of Computer Science, Zhejiang University,China November 18,2006 November 18,2006.
Enabling Access to Sound Archives through Integration, Enrichment and Retrieval WP3 – Retrieval systems.
Knowledge Science & Engineering Institute, Beijing Normal University, Analyzing Transcripts of Online Asynchronous.
Information Retrieval in Practice
Prof.dr. Inald Lagendijk, Coordinator Delft University of Technology Faculty of Electrical Engineering, Mathematics and Computer Science 1 Network of Excellence.
Using Multiple Synchronized Views Presenter: Teklu Urgessa Efficient Video Browsing.
MediaEval Workshop 2011 Pisa, Italy 1-2 September 2011.
Bridge Semantic Gap: A Large Scale Concept Ontology for Multimedia (LSCOM) Guo-Jun Qi Beckman Institute University of Illinois at Urbana-Champaign.
Multimedia Databases (MMDB)
RIAO video retrieval systems. The Físchlár-News-Stories System: Personalised Access to an Archive of TV News Alan F. Smeaton, Cathal Gurrin, Howon.
Information Systems & Semantic Web University of Koblenz ▪ Landau, Germany Semantic Web - Multimedia Annotation – Steffen Staab
Department of Computer Science and Engineering, CUHK 1 Final Year Project 2003/2004 LYU0302 PVCAIS – Personal Video Conference Archives Indexing System.
Digital Broadcasting Research Division Broadcasting media Research Group TV-Anytime Metadata Authoring Tool Jung Won Kang Digital Broadcasting Research.
Thanks to Bill Arms, Marti Hearst Documents. Last time Size of information –Continues to grow IR an old field, goes back to the ‘40s IR iterative process.
Markup and Validation Agents in Vijjana – A Pragmatic model for Self- Organizing, Collaborative, Domain- Centric Knowledge Networks S. Devalapalli, R.
Multimodal Information Analysis for Emotion Recognition
Understanding The Semantics of Media Chapter 8 Camilo A. Celis.
Research Projects 6v81 Multimedia Database Yohan Jin, T.A.
Networked Audio Visual Systems and Home Platforms ADMIRE-P at Med-e-Tel 2005 April 6-8, Application of Video Technologies and Pattern Recognition.
1 Applications of video-content analysis and retrieval IEEE Multimedia Magazine 2002 JUL-SEP Reporter: 林浩棟.
Automatic Video Tagging using Content Redundancy Stefan Siersdorfer 1, Jose San Pedro 2, Mark Sanderson 2 1 L3S Research Center, Germany 2 University of.
Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #15 Secure Multimedia Data.
Bachelor of Engineering In Image Processing Techniques For Video Content Extraction Submitted to the faculty of Engineering North Maharashtra University,
Data Mining, ICDM '08. Eighth IEEE International Conference on Duy-Dinh Le National Institute of Informatics Hitotsubashi, Chiyoda-ku Tokyo,
M4 / September Integrating multimodal descriptions to index large video collections M4 meeting – Munich Nicolas Moënne-Loccoz, Bruno Janvier,
Semantic Extraction and Semantics-Based Annotation and Retrieval for Video Databases Authors: Yan Liu & Fei Li Department of Computer Science Columbia.
Digital Video Library Network Supervisor: Prof. Michael Lyu Student: Ma Chak Kei, Jacky.
Pascal Kelm Technische Universität Berlin Communication Systems Group Thursday, 2 April 2009 Video Key Frame Extraction for image-based Applications.
Behrooz ChitsazLorrie Apple Johnson Microsoft ResearchU.S. Department of Energy.
Text Information Management ChengXiang Zhai, Tao Tao, Xuehua Shen, Hui Fang, Azadeh Shakery, Jing Jiang.
Competence Centre on Information Extraction and Image Understanding for Earth Observation PLATO for Information Mining in Satellite Imagery Soufiane RITAL,
ELAN as a tool for oral history CLARIN Oral History Workshop Oxford Sebastian Drude CLARIN ERIC 18 April 2016.
LREC – Workshop on Crossing media for Improved Information Access, Genova, Italy, 23 May Cross-Media Indexing in the Reveal-This System Murat Yakici,
Visual Information Retrieval
Automatic Video Shot Detection from MPEG Bit Stream
Multimedia and Vision Lab, Queen Mary,
Multimedia Information Retrieval
Searching and browsing through fragments of TED Talks
Multimedia Information Retrieval
Presentation transcript:

Prof. Thomas Sikora Technische Universität Berlin Communication Systems Group Thursday, 2 April 2009 Integration Activities in “Tools for Tag Generation“

Core partners: TUB (coord.), EPFL, TUD, QMUL Aims: Generation of tags and metadata (signal processing and/or users’ annotation) Keyframe extraction for image search engines Video classification, clustering and summarization Propagation of tags (object detection/ similarity) 2 Goals

Integration Activities in “Tools for Tag Generation“ Preparation: Setup database from unstructured channel “Travel” on YouTube.com –100 videos + affiliated metadata (keywords, comments, user information etc.) –Annotation of shot boundaries (by TUD and TUB) 3 Database

Integration Activities in “Tools for Tag Generation“ 4 Youtube Downloader Tool also used by the IRP “Social Media Data Collection“

Integration Activities in “Tools for Tag Generation“ 5 Youtube Downloader Information also interesting for IRP “Social Media Data Collection“

Integration Activities in “Tools for Tag Generation“ -Integration of different expertise: -Image search engines, audio signal processing, video signal processing and text detection/ recognition 6 Overview

Integration Activities in “Tools for Tag Generation“ 7 Overview Shot Subshot Key Frame Video stream: 1.Temporal segmentation Detection of hard cut, fade and dissolve 2.Subshot boundary detection Special importance for single shot videos and for long shots Detection of new visual content appearing through camera operations or undetected transitions. 3.Extraction of key frames efficient key frame extraction as a handshake between existing image search engines and video domain

Integration Activities in “Tools for Tag Generation“ 8 Key Frame Extraction

Integration Activities in “Tools for Tag Generation“ 9 Future Work

Integration Activities in “Tools for Tag Generation“ 10 Future Work Key framing [TUB] + Object Replica Detection [EPFL] + Reranking [TUD] + others

Integration Activities in “Tools for Tag Generation“ 11 Future Work Audio-Visual Source Localization in Videos [EPFL] localization can be used for automatic tag generation/propagation

Integration Activities in “Tools for Tag Generation“ 12 Future Work Quality Tags [EPFL] on Key Frames [TUB] Key frames are used to produce quality tag by no reference video quality assessment.

Integration Activities in “Tools for Tag Generation“ 13 Future Work Multimodal Video Reranking [TU Delft] Task: Retrieval of shots treating a particular topic (i.e., a semantic theme) Already done: Search over speech transcripts is improved by pseudo- relevance feedback. Selection of feedback documents is informed by visual features. Future Work: Expand reranking ideas developed on VideoCLEF 2008 dataset to YouTube-like video collections. Reranked results list

Integration Activities in “Tools for Tag Generation“ 14 Conclusion IRP continues until end May 2009 So far very good research progress – new tools Good integration of partners Tools of much interest and use for other IRP‘s Key framing will be finalized end of May Clustering will continue in new IRP Demos: