Classificação de Imagens WEB Utilizando Combinação de Classificadores (WEB Image Classification using Classifier Combination)

Pedro Rodolfo Kalva (, Fabricio Enembreck (, Alessandro Lameiras Koerich (

1HSBC Bank Brasil
2Pontifícia Universidade Católica do Paraná

This paper appears in: Revista IEEE América Latina

Publication Date: Dec. 2008
Volume: 6,   Issue: 7 
ISSN: 1548-0992

This paper presents a novel method for the classification of images that combines information extracted from the images and contextual information. The main hypothesis is that contextual information related to an image can contribute in the image classification process. Webpages containing images and text were collected and stored in an organized and structured fashion to build a database. First, independent classifiers were designed to deal with images and text. From the images were extracted several features like color, shape and texture. These features combined form feature vectors which are used together with a neural network classifier. On the other hand, contextual information is processed and used together with a Naïve Bayes classifier. At the end, the outputs of both classifiers are combined through different rules. Experimental results on a database of more than 5,000 images have shown that the combination of classifiers provides a meaningful improvement (about 16%) in the correct image classification rate relative to the results provided by the neural network based image classifier which does not use contextual information.

Index Terms:
Classifier combination, image classification, CBIR   

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