Waterpixels and their application to image segmentation learning. (Waterpixels et leur application a l'apprentissage statistique de la segmentation)
In this work, we would like to provide a general method for automatic semantic segmentation, which could adapt itself to any image database in order to be directly used by non experts in im age analysis (such as biologists) To address this problem, we first propose to use pixel classification, a classic approach based on supervised learning, where the aim is to assign to each pixel the label of the object it belongs to. Features describing each pixel properties, and which are used to determine the class label, are often computed on a fixed shape support (such as a centered window) which leads, in particular, to misclassifications on object contours. Therefore, we consider another support which is wider than the pixel itself and adapts to the image content: the superpixel. Superpixels are homogeneous and rather regular regions resulting from a low level segmen tation. We propose a new superpixel generation method based on the watershed, the waterpixels, which are efficient, fast to compute and easy to handle by the user. They are then inserted in the classification pipeline, either in replacement of pixels to be classified, or as relevant supports to compute the features, called Superpixel Adaptive Features (SAF) This second approach consti tutes a general segmentation method whose pertinence is qualitatively and quantitatively evaluated on three databases from the biological field.