We propose an hybrid and probabilistic classification of image regions belonging to scenes primarily containing natural objects, e.g. sky, trees, etc. as a first step in solving the problem of scene context generation. Therefore, we will focus our work in the problem of image regions labeling to classify every pixel of a given image into one of several predefined classes. Our proposal begins with a top-down control to find the core of objects, which allow us to update the learned models. Moreover, they become the starting seeds for the growing of a set of concurrent active regions which, considering the own region model as well as region and boundary information, obtain an accurate recognition of known regions. Next, a general segmentation extracts the unknown regions by a bottom-up strategy. Finally, a last stage exploits the contextual information to classify initially unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Experimental results on a wide set of outdoor scene images are shown to evaluate and compare our proposal.
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