We propose here a strategy for the automatic annotation of outdoor photographs. Images are segmented in
homogeneous regions which may be then assigned to seven different classes: sky, vegetation, snow, water, ground,
street, and sand. These categories allows for content-aware image processing strategies. Our annotation strategy
uses a normalized cut segmentation to identify the regions to be classified by a multi-class Support Vector
Machine. The strategy has been evaluated on a set of images taken from the LabelMe dataset.