  Feature extraction and matching are among central problems of computer
vision. It is inefficent to search features over all locations and scales.
Neurophysiological evidence shows that to locate objects in a digital image the
human visual system employs visual attention to a specific object while
ignoring others. The brain also has a mechanism to search from coarse to fine.
In this paper, we present a feature extractor and an associated hierarchical
searching model to simulate such processes. With the hierarchical
representation of the object, coarse scanning is done through the matching of
the larger scale and precise localization is conducted through the matching of
the smaller scale. Experimental results justify the proposed model in its
effectiveness and efficiency to localize features.
