DYSEC


	Semantic Framework for Image Annotation and Retrieval
Home Research Overview Members Dr. Taha Osman Dhavalkumar Thakker Prof. David Al-Dabass Prof. Christophe Claramunt Dr Evtim Peytchev Publications Group publications Projects DYnamic SErvices Composition(DYSEC) Project Semantic Framework for Image Annotation and Retrieval Semantics in GIS Events 1st Workshop on Challenges and Promise of Semantic Web Services 2nd Workshop on Challenges and Promise of Semantic Web	Project Overview Affordable access to digital technology and advances in Internet communications have contributed to the unprecedented growth of digital media repositories (audio, images, and video). Retrieving relevant media from these ever-increasing repositories is an impossible task for the user without the aid of search tools. Most public image retrieval engines rely on analysing the text accompanying the image to matchmake it with the user query. Various optimisations were developed including the use of weighting systems where for instance higher regard can be given to the proximity of the keyword to the image location, or advanced text analysis techniques that use term weighting method, which relies on the proximity between the anchor to an image and each word in an HTML file. Despite the optimisation efforts, these search techniques remain hampered by the fact that they rely on free-text search that, while cost-effective to perform, can return irrelevant results as it primarily relies on the recurrence of exact words in the text accompanying the image. Any significant contribution to the accuracy of matchmaking results can be achieved only if the search engine can “comprehend” the meaning of the data that describes the stored images, for instance, if the search engine can understand that scoring is an act associated with sport activities performed by humans. Semantic annotation techniques have gained wide popularity in associating plain data with “structured” concepts that software programs can reason about. In this project we research a comprehensive semantic-based solution to image annotation and retrieval as well as deploying query expansion techniques for improving the recall rate. Project specifically targets the commercial image collections market and acknowledges their requirements for high quality recall without sacrificing the performance of the retrieval process.

Semantic Framework for Image Annotation
and Retrieval

Home

Research Overview

Members

Dr. Taha Osman
Dhavalkumar Thakker
Prof. David Al-Dabass
Prof. Christophe Claramunt
Dr Evtim Peytchev

Publications

Group publications

Projects

DYnamic SErvices Composition(DYSEC) Project

Semantic Framework for Image Annotation and Retrieval

Semantics in GIS

Events

1st Workshop on Challenges and Promise of Semantic Web Services

2nd Workshop on Challenges and Promise of Semantic Web

Project Overview

Affordable access to digital technology and advances in Internet communications have contributed to the unprecedented growth of digital media repositories (audio, images, and video). Retrieving relevant media from these ever-increasing repositories is an impossible task for the user without the aid of search tools. Most public image retrieval engines rely on analysing the text accompanying the image to matchmake it with the user query. Various optimisations were developed including the use of weighting systems where for instance higher regard can be given to the proximity of the keyword to the image location, or advanced text analysis techniques that use term weighting method, which relies on the proximity between the anchor to an image and each word in an HTML file. Despite the optimisation efforts, these search techniques remain hampered by the fact that they rely on free-text search that, while cost-effective to perform, can return irrelevant results as it primarily relies on the recurrence of exact words in the text accompanying the image.

Any significant contribution to the accuracy of matchmaking results can be achieved only if the search engine can “comprehend” the meaning of the data that describes the stored images, for instance, if the search engine can understand that scoring is an act associated with sport activities performed by humans. Semantic annotation techniques have gained wide popularity in associating plain data with “structured” concepts that software programs can reason about. In this project we research a comprehensive semantic-based solution to image annotation and retrieval as well as deploying query expansion techniques for improving the recall rate. Project specifically targets the commercial image collections market and acknowledges their requirements for high quality recall without sacrificing the performance of the retrieval process.