MUSCLE/ImageCLEF workshop 2005 Extracting an Ontology of Portrayable Objects from WordNet Atomic Energy Agency of France (CEA) LIC2M (Multilingual Multimedia.

Slides:

Advertisements

Similar presentations

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

Advertisements

eClassifier: Tool for Taxonomies

Building Wordnets Piek Vossen, Irion Technologies.

Presented by Xinyu Chang

ImageCLEF breakout session Please help us to prepare ImageCLEF2010.

Large dataset for object and scene recognition A. Torralba, R. Fergus, W. T. Freeman 80 million tiny images Ron Yanovich Guy Peled.

Research topics Semantic Web - Spring 2007 Computer Engineering Department Sharif University of Technology.

Event prediction CS 590v. Applications Video search Surveillance – Detecting suspicious activities – Illegally parked cars – Abandoned bags Intelligent.

Tour the World: building a web-scale landmark recognition engine ICCV 2009 Yan-Tao Zheng1, Ming Zhao2, Yang Song2, Hartwig Adam2 Ulrich Buddemeier2, Alessandro.

Semantic Web and Web Mining: Networking with Industry and Academia İsmail Hakkı Toroslu IST EVENT 2006.

Lecture 28: Bag-of-words models

Measuring Semantic Similarity between Words Using Web Search Engines Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka Topic  Semantic similarity measures.

CS335 Principles of Multimedia Systems Content Based Media Retrieval Hao Jiang Computer Science Department Boston College Dec. 4, 2007.

March 17, 2008SAC WT Hermes: a Semantic Web-Based News Decision Support System* Flavius Frasincar Erasmus University Rotterdam.

The Informative Role of WordNet in Open-Domain Question Answering Marius Paşca and Sanda M. Harabagiu (NAACL 2001) Presented by Shauna Eggers CS 620 February.

AceMedia Personal content management in a mobile environment Jonathan Teh Motorola Labs.

Semantics For the Semantic Web: The Implicit, the Formal and The Powerful Amit Sheth, Cartic Ramakrishnan, Christopher Thomas CS751 Spring 2005 Presenter:

1 Information Retrieval and Extraction 資訊檢索與擷取 Chia-Hui Chang, Assistant Professor Dept. of Computer Science & Information Engineering National Central.

Information Retrieval and Extraction 資訊檢索與擷取 Chia-Hui Chang National Central University

Presented by Zeehasham Rasheed

Ranking by Odds Ratio A Probability Model Approach let be a Boolean random variable: document d is relevant to query q otherwise Consider document d as.

Introduction to WEKA Aaron 2/13/2009. Contents Introduction to weka Download and install weka Basic use of weka Weka API Survey.

High-Performance Digital Library Classification Systems: PI: Hsinchun Chen, The University of Arizona From Information Retrieval to Knowledge Management.

Predicting Matchability - CVPR 2014 Paper -

ImageNet: A Large-Scale Hierarchical Image Database

Ontology Learning and Population from Text: Algorithms, Evaluation and Applications Chapters Presented by Sole.

LLNL-PRES This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Analysing Crime-Scene Reports Katerina Pastra and Horacio Saggion University of Sheffield Scene of Crime Information System.

What Are the High-Level Concepts with Small Semantic Gaps? CS 4763 Multimedia System, Spring 2008.

Collective Vision: Using Extremely Large Photograph Collections Mark Lenz CameraNet Seminar University of Wisconsin – Madison January 26, 2010 Acknowledgments:

CONTI’2008, 5-6 June 2008, TIMISOARA 1 Towards a digital content management system Gheorghe Sebestyen-Pal, Tünde Bálint, Bogdan Moscaliuc, Agnes Sebestyen-Pal.

Intelligent Systems Lecture 23 Introduction to Intelligent Data Analysis (IDA). Example of system for Data Analyzing based on neural networks.

CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.

WP5.4 - Introduction  Knowledge Extraction from Complementary Sources  This activity is concerned with augmenting the semantic multimedia metadata basis.

An Integrated Approach to Extracting Ontological Structures from Folksonomies Huairen Lin, Joseph Davis, Ying Zhou ESWC 2009 Hyewon Lim October 9 th, 2009.

Multimedia Databases (MMDB)

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Domain-specific Web Corpora and their Applications Gregor Erbach.

1 LIST – DTSI – Service Réalité virtuelle, Cognitique et Interfaces sensorielles A Conceptual Approach to Web Image Retrieval Adrian Popescu Gregory Grefenstette.

Introduction to Web Mining Spring What is data mining? Data mining is extraction of useful patterns from data sources, e.g., databases, texts, web,

Ontologies and Lexical Semantic Networks, Their Editing and Browsing Pavel Smrž and Martin Povolný Faculty of Informatics,

VIKEF – Take the VIKEF train towards smart services …

Video Google: A Text Retrieval Approach to Object Matching in Videos Josef Sivic and Andrew Zisserman.

80 million tiny images: a large dataset for non-parametric object and scene recognition CS 4763 Multimedia Systems Spring 2008.

Collective Vision: Using Extremely Large Photograph Collections Mark Lenz CameraNet Seminar University of Wisconsin – Madison February 2, 2010 Acknowledgments:

Research Projects 6v81 Multimedia Database Yohan Jin, T.A.

CSM06 Information Retrieval Lecture 1a – Introduction Dr Andrew Salway

Pattern Recognition April 19, 2007 Suggested Reading: Horn Chapter 14.

Data Mining In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. Data mining tools automatically search the data.

Evaluating Semantic Metadata without the Presence of a Gold Standard Yuangui Lei, Andriy Nikolov, Victoria Uren, Enrico Motta Knowledge Media Institute,

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei Dept. of Computer Science, Princeton University, USA CVPR ImageNet1.

Tijana Janjusevic Multimedia and Vision Group, Queen Mary, University of London Clustering of Visual Data using Ant-inspired Methods Supervisor: Prof.

Next Generation Search Engines Ehsun Daroodi 1 Feb, 2003.

Mining real world data Web data. World Wide Web Hypertext documents –Text –Links Web –billions of documents –authored by millions of diverse people –edited.

Levels of Image Data Representation 4.2. Traditional Image Data Structures 4.3. Hierarchical Data Structures Chapter 4 – Data structures for.

1 A Web Search Engine-Based Approach to Measure Semantic Similarity between Words Presenter: Guan-Yu Chen IEEE Trans. on Knowledge & Data Engineering,

1Ellen L. Walker Category Recognition Associating information extracted from images with categories (classes) of objects Requires prior knowledge about.

Graduate School of Informatics Kyoto University, November 21, 2001 Technologies of the Interspace Peer-Peer Semantic Indexing Bruce Schatz CANIS Laboratory.

Image Classification for Automatic Annotation

Image Classification over Visual Tree Jianping Fan Dept of Computer Science UNC-Charlotte, NC

Data Mining By: Johan Johansson. Mining Techniques Association Rules Association Rules Decision Trees Decision Trees Clustering Clustering Nearest Neighbor.

MULTIMEDIA DATA MODELS AND AUTHORING

Semantic search-based image annotation Petra Budíková, FI MU CEMI meeting, Plzeň,

An Ontology-based Automatic Semantic Annotation Approach for Patent Document Retrieval in Product Innovation Design Feng Wang, Lanfen Lin, Zhou Yang College.

Data mining in web applications

Lecture #11: Ontology Engineering Dr. Bhavani Thuraisingham

Data Mining Techniques So Far…

Basic Intro Tutorial on Machine Learning and Data Mining

Large Scale Image Deduplication

CSE 635 Multimedia Information Retrieval

Adaboost for faces. Material

Presentation transcript:

MUSCLE/ImageCLEF workshop 2005 Extracting an Ontology of Portrayable Objects from WordNet Atomic Energy Agency of France (CEA) LIC2M (Multilingual Multimedia Knowledge Engineering Laboratory) BP6, 18 Route du Panorama 92265, Fontenay aux Roses, France S. Zinger, C. Millet, B. Mathieu, G. Grefenstette, P. Hède, P.-A. Moëllic

MUSCLE/ImageCLEF workshop Goal: creation of a large-scale image ontology WordNet lexical resourses Image collections acquisition through web- based image mining

MUSCLE/ImageCLEF workshop Building a large-scale image ontology for object recognition: list of portrayable objects WordNet lexical resources basis of ontology

MUSCLE/ImageCLEF workshop Building a large-scale image ontology for object recognition: visual features semantic filtering clustering classification web-based image mining large-scale visual dictionary

MUSCLE/ImageCLEF workshop Pruning approach to WordNet ENTITY has a distinct separate existence (living or nonliving) OBJECT physical object (a tangible a visible entity) simplifying connections selecting branches object  living thing  life  wildlife object  living thing  plant  …  tree  tree of knowledge object  artifact  creation  classic deleted

MUSCLE/ImageCLEF workshop Extraction from top-level ontology of portrayable objects ENTITY object living thing natural objectartifactfloater organismcelestial body rock articlecommodity consumer goods 102 nodes in total

MUSCLE/ImageCLEF workshop VIKA (Visual Kataloguer) indexing (PIRIA – LIC2M) clustering (shared nearest neighbor) visualisation of clusters web-image search engine (Alltheweb)

MUSCLE/ImageCLEF workshop List of portrayable objects (24000 items) queries to the web (e.g. google image) VIKA (Visual Kataloguer)

MUSCLE/ImageCLEF workshop Composition of queries: upper node + word identifying portrayable object Example of queries: kino tree red sandalwood tree carib wood tree Japanese pagoda tree palm tree... Japanese pagoda buildings Japanese pagoda tree trees

MUSCLE/ImageCLEF workshop VIKA (Visual Kataloguer)

MUSCLE/ImageCLEF workshop Web-image search at presentDesired results query « chair»

MUSCLE/ImageCLEF workshop Future work: face detection (adaboost learning) – to filter web-image search results semantically (images of objects without people) testing VIKA system performances automatic cluster classification – ignoring irrelevant clusters introducing new connections to the ontology: vision principles (scale), co-occurrence rules

MUSCLE/ImageCLEF workshop Future work Vision principles (scale)

MUSCLE/ImageCLEF workshop Future work Co-occurrence rules