Evaluating the location of hot spots in interactive scenes using the 3R toolbox
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
RELIEF: combining expressiveness and rapidity into a single system
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Modelling multimodal interaction: A theory-based technique for design analysis support
INTERACT '97 Proceedings of the IFIP TC13 Interantional Conference on Human-Computer Interaction
Modeling, Indexing and Retrieving Images Using Conceptual Graphs
DEXA '98 Proceedings of the 9th International Conference on Database and Expert Systems Applications
A Flexible Weighting Scheme for Multimedia Documents
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
EMIR2: An Extended Model for Image Representation and Retrieval
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
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Experimental evidence accumulated over the past years indicated the importance of the ranking process in information retrieval. As for the textual documents, image ranking is a task that involves different parameters. They depend on the intrinsic characteristics of an image, but also on the indexing language used for representing its semantic content. We developed a weighting model that combines these parameters in a general scheme. Finding the best balance between the parameters is not straight-forward. Different parameter combinations leads to different rankings, which may be more or less accepted by the users. In this paper, we choose a set of test queries and present the impact of the parameters on the rank of each image. Different combinations are discussed, and the best combination is specified. For the evaluation, we follow a user-oriented approach, and compare the ranking provided by each parameter combination to the ranking given by human judgment. This is a step toward a user-centered image retrieval system, which will dynamically adapt to the user's profile and preferences.