Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A constraint-based approach to shape management in multimedia databases
Multimedia 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
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
RF*IPF: A Weighting Scheme for Multimedia Information Retrieval
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Structured knowledge representation for image retrieval
Journal of Artificial Intelligence Research
A relational vector space model using an advanced weighting scheme for image retrieval
Information Processing and Management: an International Journal
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A star-graph is a conceptual graph that contains a single relation, with some concepts linked to it. They are elementary pieces of information describing combinations of concepts. We use star-graphs as descriptors - or index terms - for image content representation. This allows for relational indexing and expression of complex user needs, in comparison to classical text retrieval, where simple keywords are generally used as document descriptors. In classical text retrieval, the keywords are weighted to give emphasis to good document descriptors and discriminators where the most popular weighting schemes are based on variations of tf.idf. In this paper, we present an extension of tf.idf, introducing a new weighting scheme suited for star-graphs. This weighting scheme is based on a local analysis of star-graphs indexing a document and a global analysis of star-graphs across the whole collection. We show and discuss some preliminary results evaluating the performance of this weighting scheme applied to image retrieval.