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In the last few decades several techniques for image content extraction, often based on segmentation, have been
proposed. It has been suggested that under the assumption of very general image content, segmentation becomes
unstable and classification becomes unreliable. According to recent psychological theories, certain image regions
attract the attention of human observers more than others and, generally, the image main meaning appears
concentrated in those regions. Initially, regions attracting our attention are perceived as a whole and hypotheses
on their content are formulated; successively the components of those regions are carefully analyzed and a more
precise interpretation is reached. It is interesting to observe that an image decomposition process performed
according to these psychological visual attention theories might present advantages with respect to a traditional
segmentation approach. In this paper we propose an automatic procedure generating image decomposition based
on the detection of visual attention regions. A new clustering algorithm taking advantage of the Delaunay-
Voronoi diagrams for achieving the decomposition target is proposed. By applying that algorithm recursively,
starting from the whole image, a transformation of the image into a tree of related meaningful regions is obtained
(Attention Tree). Successively, a semantic interpretation of the leaf nodes is carried out by using a structure of
Neural Networks (Neural Tree) assisted by a knowledge base (Ontology Net). Starting from leaf nodes, paths
toward the root node across the Attention Tree are attempted. The task of the path consists in relating the
semantics of each child-parent node pair and, consequently, in merging the corresponding image regions. The
relationship detected in this way between two tree nodes generates, as a result, the extension of the interpreted
image area through each step of the path. The construction of several Attention Trees has been performed and
partial results will be shown.
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