Information Fusion for Combining Visual and Textual Image Retrieval

In this paper, classical approaches such as maximum combinations (combMAX), sum combinations (comb-SUM) and the product of the maximum and a non–zero number (combMNZ) were employed and the trade–off between two fusion effects (chorus and dark horse effects) was studied based on the sum of n maximums. Various normalization strategies were tried out. The fusion algorithms are evaluated using the best four visual and textual runs of the ImageCLEF medical image retrieval task 2008 and 2009. The results show that fused runs outperform the best original runs and multi-modality fusion statistically outperforms single modality fusion. The logarithmic rank penalization shows to be the most stable normalization. The dark horse effect is in competition with the chorus effect and each of them can produce best fusion performance depending on the nature of the input data.