A hierarchical approach to feature extraction and grouping

In this paper, the problem of extracting and grouping image features from c omplex scenes is solved by a hierarchical approach based on two main proces ses: voting and clustering. Voting is performed for assigning a score to bo th global and local features. The score represents the evidential support p rovided by input data for the presence of a feature. Clustering aims at ind ividuating a minimal set of significant local features by grouping together simpler correlated observations, It is based on a spatial relation between simple observations on a fixed level, i.e., the definition of a distance i n an appropriate space. As the multilevel structure of the system implies t hat input data for an intermediate level are outputs of the lower level, vo ting can be seen as a functional representation of the "part-of" relation b etween features at different abstraction levels. The proposed approach has been tested on both synthetic and real images and compared with other exist ing feature grouping methods.