Abstract : This paper presents a component of a content based image retrieval system dedicated to let a user define the indexing terms used later during retrieval. A user inputs a indexing term name, image examples and counter-examples of the term,and the system learns a model of the concept as well as a similarity measure for this term. The similarity measure is based on weights reflecting the importance of each low-level feature extracted from the images. The system computes these weights using a genetic algorithm. Rating a particular similarity measure is done by clustering the examples and counter-examples using these weights and computing the quality of the obtained clusters. Experiments are conducted and results are presented on a set of 600 images.
https://hal.inria.fr/hal-00953933
Contributeur : Marie-Christine Fauvet
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Soumis le : lundi 3 mars 2014 - 12:49:24
Dernière modification le : mardi 24 avril 2018 - 13:29:38
Document(s) archivé(s) le : samedi 31 mai 2014 - 10:51:42
Stéphane Bissol, Philippe Mulhem, Yves Chiaramella. Dynamic Learning of Indexing Concepts for Home Image Retrieval. Content-Based Multimedia Indexing (CBMI2003), 2003, Rennes, France. pp.87--93, 2003. 〈hal-00953933〉
