Modeling, classifying and annotating weakly annotated images using Bayesian network

Sabine Barrat 1 Salvatore Tabbone 1
1 QGAR - Querying Graphics through Analysis and Recognition
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We propose a probabilistic graphical model to represent weakly annotated images. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in classification and automatic annotation of images. The experimental results, obtained from a database of more than 30000 images, by combining visual and textual information, show an improvement by 50.5% in terms of recognition rate against only visual information classication. Taking into account semantic relations between keywords improves the recognition rate by 10.5% and the mean rate of good annotations by 6.9%. The proposed method is experimentally competitive with the state-of-art classifiers.
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Communication dans un congrès
Tenth International Conference on Document Analysis and Recognition - ICDAR'2009, Jul 2009, Barcelona, Spain. 2009
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https://hal.inria.fr/inria-00389496
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Dernière modification le : jeudi 11 janvier 2018 - 06:19:59
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Sabine Barrat, Salvatore Tabbone. Modeling, classifying and annotating weakly annotated images using Bayesian network. Tenth International Conference on Document Analysis and Recognition - ICDAR'2009, Jul 2009, Barcelona, Spain. 2009. 〈inria-00389496〉

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