Internet Multimedia Search and Mining

Abstract: In this chapter we will present an overview of data fusion , and how it can beapplied to the task of internet multimedia search, specifically content-based multimediasearch. This chapter will primairly be focused on the weighted combination of rankedresults from different retrieval experts, to formulate a final ranking for some given content-based information need. The types of data under examination in this chapter are low-level multimedia features, such as colour histograms, edge detection etc. This chapterwill examine the key attributes which impact upon data fusion, and through an empiricalinvestigation present the best formulations that should be used when implementing datafusion. Furthermore this chapter conducts a review of current approaches to handlingthe generation of weights for data fusion, including query-class approaches, discriminativeclassification and relevance feedback. Introduction The availability of information resources on the internet has ushered in the ‘Web 2.0’phenomenon, spearheaded by websites which are ‘mashups’. These are websites whichcombine forms of data from multiple external sources in order to fulfill some form ofinformation need. Often these forms of data will be multimedia, and allow for the creationof rich, informative sources of information. In many ways, the ‘mashup’ can be seen as anextension of an earlier web phenomenon, the meta-search engine which still exists today,as seen in a search service such as ‘dogpile.com’. The meta-search engine takes in a singleinformation query and combines the outputs of multiple other external search services toformulate a single response to that query. Both of these tasks are executing an operationknown as