Visiwords John Tait Chief Scientific Officer. Warning A few half formed ideas from the world of image and video indexing which may be of interest to MT.

Slides:



Advertisements
Similar presentations
Bringing It All Together: An Academic Viewpoint (What is needed and what is likely to come next?) Association of Information and Dissemination Centers.
Advertisements

Making Links Fundamentals of Hypertext and Hypermedia Dr Nicholas Gibbins
Multilingual Text Retrieval Applications of Multilingual Text Retrieval W. Bruce Croft, John Broglio and Hideo Fujii Computer Science Department University.
Predicting Text Quality for Scientific Articles Annie Louis University of Pennsylvania Advisor: Ani Nenkova.
Information retrieval Finding relevant data using irrelevant keys Example: database of photographic images sorted by number, date. DBMS: Well structured.
Agenda Introduction Bag-of-words models Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition Recognition-based.
Advanced Database Applications Database Indexing and Data Mining CS591-G1 -- Fall 2001 George Kollios Boston University.
SBU Digital Media CSE 690 Internet Vision Organizational Meeting Tamara Berg Assistant Professor SUNY Stony Brook.
 Fatemeh Lashkari UNB University May 7 th  Indexing  Semantic Search  Semantic Search Architecture  Index process  Index Maintenance.
Utilising software to enhance your research Eamonn Hynes 5 th November, 2012.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
Indexing Knowledge Daniel Vasicek 2014 March 27 Introduction Basic topic is : All Human Knowledge Who Cares? Simple Examples.
DFCI Boston: Using the Weighted Histogram Analysis Method (WHAM) in cancer biology and the Yeast Protein Databank (YPD); Latent Dirichlet Analysis (LDA)
Citation Recommendation 1 Web Technology Laboratory Ferdowsi University of Mashhad.
Multimedia Databases (MMDB)
Exploiting Ontologies for Automatic Image Annotation M. Srikanth, J. Varner, M. Bowden, D. Moldovan Language Computer Corporation
Information Systems & Semantic Web University of Koblenz ▪ Landau, Germany Semantic Web - Multimedia Annotation – Steffen Staab
HANOLISTIC: A HIERARCHICAL AUTOMATIC IMAGE ANNOTATION SYSTEM USING HOLISTIC APPROACH Özge Öztimur Karadağ & Fatoş T. Yarman Vural Department of Computer.
Sampletalk Technology Presentation Andrew Gleibman
Which of the two appears simple to you? 1 2.
Category Discovery from the Web slide credit Fei-Fei et. al.
Research Projects 6v81 Multimedia Database Yohan Jin, T.A.
A Model for Learning the Semantics of Pictures V. Lavrenko, R. Manmatha, J. Jeon Center for Intelligent Information Retrieval Computer Science Department,
For Monday Read chapter 24, sections 1-3 Homework: –Chapter 23, exercise 8.
For Friday Finish chapter 24 No written homework.
For Monday Read chapter 26 Last Homework –Chapter 23, exercise 7.
Object Recognition Part 2 Authors: Kobus Barnard, Pinar Duygulu, Nado de Freitas, and David Forsyth Slides by Rong Zhang CSE 595 – Words and Pictures Presentation.
Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #15 Secure Multimedia Data.
Effective Automatic Image Annotation Via A Coherent Language Model and Active Learning Rong Jin, Joyce Y. Chai Michigan State University Luo Si Carnegie.
Exploiting Ontologies for Automatic Image Annotation Munirathnam Srikanth, Joshua Varner, Mitchell Bowden, Dan Moldovan Language Computer Corporation SIGIR.
Image Retrieval John Tait University of Sunderland, UK.
Ping-Tsun Chang Intelligent Systems Laboratory Computer Science and Information Engineering National Taiwan University Combining Unsupervised Feature Selection.
Mentor Prof. Amitabha Mukerjee Deepak Pathak Kaustubh Tapi 10346
Relevance Feedback in Image Retrieval System: A Survey Tao Huang Lin Luo Chengcui Zhang.
Competence Centre on Information Extraction and Image Understanding for Earth Observation PLATO for Information Mining in Satellite Imagery Soufiane RITAL,
1 Knowledge-Based Medical Image Indexing and Retrieval Caroline LACOSTE Joo Hwee LIM Jean-Pierre CHEVALLET Daniel RACOCEANU Nicolas Maillot Image Perception,
CIS750 – Seminar in Advanced Topics in Computer Science Advanced topics in databases – Multimedia Databases V. Megalooikonomou Link mining ( based on slides.
The Ways we can encode… Visual Encoding: the encoding of picture images. Acoustic Encoding: the encoding of sound, especially the sounds of words. Semantic.
Concept-Based Analysis of Scientific Literature Chen-Tse Tsai, Gourab Kundu, Dan Roth UIUC.
By Akhilesh K. Sinha Nishant Singh Supervised by Prof. Amitabha Mukerjee Video Surveillance of Basketball Matches and Goal Detection Indian Institute of.
Data Mining in Germany IIM Conference, Oct. 24, 2012 Gottfried Schwarz, DLR > Lecture > Author Document > Datewww.DLR.de Chart 1.
3D Motion Classification Partial Image Retrieval and Download Multimedia Project Multimedia and Network Lab, Department of Computer Science.
Multi-Class Sentiment Analysis with Clustering and Score Representation Yan Zhu.
Face Detection 蔡宇軒.
MULTIMEDIA SYSTEMS CBIR & CBVR. Schedule Image Annotation (CBIR) Image Annotation (CBIR) Video Annotation (CBVR) Video Annotation (CBVR) Few Project Ideas.
Machine learning & object recognition Cordelia Schmid Jakob Verbeek.
Content Based Image Retrieval Thanks to John Tait
RECENT TRENDS IN SMT By M.Balamurugan, Phd Research Scholar,
Content-Based Image Retrieval
Applying Deep Neural Network to Enhance EMPI Searching
3D Motion Classification Partial Image Retrieval and Download
Clustering of Web pages
Semantics Sensitive Segmentation and Annotation of Natural Images
PowerPoint Assignment
Restricted Boltzmann Machines for Classification
ICDIS 2018 Intelligence and Security
When the subjects of metadata embrace the statistical learning
Data Analyzing Artificial Intelligence (AI)
When the subjects of metadata embraces the statistical learning
What is Pattern Recognition?
Finding Clusters within a Class to Improve Classification Accuracy
convolutional neural networkS
convolutional neural networkS
Multimedia Information Retrieval
Ying Dai Faculty of software and information science,
Data Mining, Machine Learning, Data Analysis, etc. scikit-learn
How To Extend the Training Data
CSC 578 Neural Networks and Deep Learning
ADVANCED TOPICS IN INFORMATION RETRIEVAL AND WEB SEARCH
Information Retrieval
Presentation transcript:

Visiwords John Tait Chief Scientific Officer

Warning A few half formed ideas from the world of image and video indexing which may be of interest to MT people Not original ideas (apart from I think the link) In fact a line of work which derives originally from MT

The nub Unsupervised Clustering of bundles of features –Colour, texture from image segments –Words, phrases from sentences or paragraphs ? Associate these bundles with “translations” by supervised machine learning –Categorised images –Parallel texts

Origins “Matching Words and Pictures”: Barnard, Duygulu, Forsyth, de Freitas, Blei, Jordan. Journal of Machine Learning Research 3 (2003) “Image Classification Using Hybrid Neural Networks” Tsai, McGarry and Tait. Proceedings of the 26 th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2003), Toronto, July, pp

More or less general clusters General Concepts Specific Concepts

Visiwords Derived from Visiterms –These feature cluster nodes –A notion of an area of the “semantic field” –Remember these are colour, texture etc. for an area of an image …. No relation to language … or at least a very deep one

Matching L1 General Concepts L1 Specific Concepts L2 General Concepts L2 Specific Concepts Examples

Fast Forward to 2009 Better statistical models tuned to the data Much Bigger vocabularies of words categories … and lots of other advances

A question Is there anything like this current MT research ?

Concluding remarks I’m surprised this worked at all –Why should image data be coherent and cohesive ? But text is !!! Is this a better way to deal with unknown and changing vocabulary

Some other references A Correlation Approach for Automatic Image Annotation Hardoon, D., Saunders, C., Szedmak, S. and Shawe-Taylor, J. (2006) A Correlation Approach for Automatic Image Annotation. In: Second International Conference on Advanced Data Mining and Applications, ADMA 2006, August, Xi'an, China. Kucuktunc, O., Sevil, S. G., Tosun, A. B., Zitouni, H., Duygulu, P., and Can, F Tag Suggestr: Automatic Photo Tag Expansion Using Visual Information for Photo Sharing Websites. In Proceedings of the 3rd international Conference on Semantic and Digital Media Technologies: Semantic Multimedia (Koblenz, Germany, December , 2008). D. Duke, L. Hardman, A. Hauptmann, D. Paulus, and S. Staab, Eds. Lecture Notes In Computer Science, vol Springer-Verlag, Berlin, Heidelberg, DOI=