Exploring social tagging graph for web object classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper presents a novel method for the classifica- tion of images that combines information extracted from the images and contextual information. The main hypoth- esis is that contextual information related to an image can contribute in the image classification process. First, inde- pendent 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 Na篓ive Bayes (NB) classifier. At the end, the outputs of both classifiers are combined through heuristic rules. Ex- perimental 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 cor- rect image classification rate relative to the results provided by the NN classifier alone.