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
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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