Image retrieval by word association
This event took place on Monday 09 May 2005 at 14:00
Dr. Philip Edmonds Sharp Laboratories of Europe
Have you ever wanted to find an image or sound to illustrate an abstract concept? Or an image that is metaphorically associated with some text rather than described by the text? This talk will present research on how one can retrieve (text-annotated) images that are related in various ways to a text query, and organize them in a user interface. The method identifies various possible 'interpretations' of the input query using word sense disambiguation techniques (i.e., clustering), and generates an expanded query for each interpretation. The research is an application of lexical association scores (including co-occurrence and similarity scores). This kind of search could eventually have applications in better image search engines, or in the automatic illustration of news articles.
This event took place on Monday 09 May 2005 at 14:00
Have you ever wanted to find an image or sound to illustrate an abstract concept? Or an image that is metaphorically associated with some text rather than described by the text? This talk will present research on how one can retrieve (text-annotated) images that are related in various ways to a text query, and organize them in a user interface. The method identifies various possible 'interpretations' of the input query using word sense disambiguation techniques (i.e., clustering), and generates an expanded query for each interpretation. The research is an application of lexical association scores (including co-occurrence and similarity scores). This kind of search could eventually have applications in better image search engines, or in the automatic illustration of news articles.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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