Boosting for Image Interpretation by Using Natural Features

In this paper a research in classification of natural images by using Adaboost (adapting boosting) method is presented. This technique is used to identify the nature of the main regions in the image, that is, to identify if they are roads, trees, shades, sky, bushes or others interesting regions; image is previously segmented and each of its regions are represented by a R12 data vector (including features as color, texture and context), in at least 5 classes. The proposed methodology is presented for a multi-class classification problem and for validating our results, performances ratios between Adaboost and the support vector machines are discussed. This methodology is intent to be applied in medical imagery and in visual based navigation on natural environments; in robot navigation, very good results are obtained even in poorly color saturated images. Finally, the results are described and presented showing that Adaboost is a reliable classification technique giving slightly better performances than SVM for regions in natural images.