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1 NUS - National University of Singapore 2 IPAL - Image & Pervasive Access Lab 3 MRIM - Modélisation et Recherche d’Information Multimédia Grenoble LIG - Laboratoire d-Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique 4 CLIPS - IMAG - Communication Langagière et Interaction Personne-Système

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.

Keywords : Image Indexing Genetic Algorithms retrieval

Autor: Stéphane Bissol - Philippe Mulhem - Yves Chiaramella -

Fuente: https://hal.archives-ouvertes.fr/


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