Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Fusion Via a Linear Combination of Scores
Information Retrieval
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
Performance prediction of data fusion for information retrieval
Information Processing and Management: an International Journal
ProbFuse: a probabilistic approach to data fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Information Fusion in Multimedia Information Retrieval
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Assigning appropriate weights for the linear combination data fusion method in information retrieval
Information Processing and Management: an International Journal
Overview of the CLEF 2009 medical image retrieval track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Evaluating score normalization methods in data fusion
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Hi-index | 0.00 |
In the ImageCLEF image retrieval competition multimodal image retrieval has been evaluated over the past seven years. For ICPR 2010 a contest was organized for the fusion of visual and textual retrieval as this was one task where most participants had problems. In this paper, classical approaches such as the maximum combinations (combMAX), the sum combinations (combSUM) and the multiplication of the sum and the number of non-zero scores (combMNZ) were employed and the trade-off between two fusion effects (chorus and dark horse effects) was studied based on the sum of n maxima. 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.