Using score distributions for query-time fusion in multimediaretrieval

In this paper we present the results of our work on the analysis of multi-modal data for video Information Retrieval, where we exploit the properties of this data for query-time, automatic generation of weights for multi-modal data fusion. Through empirical testing we have observed that for a given topic, a high performing feature,that is one which achieves high relevance, will have a different distribution of document scores when compared against those that do not perform as well. These observations form the basis for our initial fusion model, which generates weights based on these properties, without the need for prior training.Our model can be used to not only combine feature data,but to also combine the results of multiple example query images and apply weights to these.Our analysis and experiments were conducted on the TRECVid 2004 and 2005 collections,making use of multiple MPEG-7 low-level features and automatic speech recognition (ASR)transcripts.Results achieved from our model achieve performance on a par with that of 'oracle' determined weights,and demonstrate the applicability of our model whilst advancing the case for further investigation of score distributions.