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KMi Seminars
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.

 
KMi Seminars
 

Knowledge Management is...


Knowledge Management
Creating learning organisations hinges on managing knowledge at many levels. Knowledge can be provided by individuals or it can be created as a collective effort of a group working together towards a common goal, it can be situated as "war stories" or it can be generalised as guidelines, it can be described informally as comments in a natural language, pictures and technical drawings or it can be formalised as mathematical formulae and rules, it can be expressed explicitly or it can be tacit, embedded in the work product. The recipient of knowledge - the learner - can be an individual or a work group, professionals, university students, schoolchildren or informal communities of interest.
Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.