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WEB Image Classification Based on the Fusion of Image and Text Classifiers
| Citations: | 5 - 0 self |
BibTeX
@MISC{Kalva_webimage,
author = {Pedro R. Kalva and Fabricio Enembreck and Ro L. Koerich},
title = {WEB Image Classification Based on the Fusion of Image and Text Classifiers},
year = {}
}
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Abstract
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. First, independent classifiers are designed to deal with images and text. From the images color, shape and texture features are extracted. These features are used with a neural network (NN) classifier to carry out image classification. On the other hand, contextual information is processed and used with a Naïve Bayes (NB) classifier. At the end, the outputs of both classifiers are combined through heuristic rules. Experimental results on a database of more than 5,000 HTML documents 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 NN classifier alone. 1
Keyphrases
web image classification text classifier contextual information na ve bayes html document meaningful improvement experimental result main hypothesis texture feature independent classifier image classification process correct image classification rate relative novel method nn classifier image classification image color heuristic rule neural network
