Comparing Dissimilarity Measures for Content-Based Image Retrieval
This event took place on Monday 07 January 2008 at 13:30
Rui Hu KMi - The Open University
Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure?s retrieval performance, on different feature spaces? In this report, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the e?ectiveness of these dissimilarity measures with six different feature spaces. Based on the experimental results, we recommend some dissimilarity measures for future use.
This event took place on Monday 07 January 2008 at 13:30
Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure?s retrieval performance, on different feature spaces? In this report, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the e?ectiveness of these dissimilarity measures with six different feature spaces. Based on the experimental results, we recommend some dissimilarity measures for future use.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Semantic Web and Knowledge Services is...
Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
Our aim is to be at the forefront of both theoretical and practical developments on the Semantic Web not only by developing theories and models, but also by building concrete applications, for a variety of domains and user communities, including KMi and the Open University itself.
List all Semantic Web and Knowledge Services Projects
Check out these Hot Semantic Web and Knowledge Services Technologies:
List all Semantic Web and Knowledge Services Technologies
