Detecting and ranking saliency for scene description

Abstract

There is a long tradition in Computational Vision research regarding Vision as an information processing task which builds up from low level image features to high level reasoning functions. As far as low level image detectors are concerned there is a plethora of techniques found in the literature, although many of them especially designed for particular applications. For natural scenes, where objects and backgrounds change frequently, finding regions of interest which were triggered by a concentration of non-accidental properties can provide a more informative and stable intermediate mechanism for scene description than just low level features. In this paper we propose such a mechanism to detect and rank salient regions in natural and cluttered images. First, a bank of Gabor filters is applied to the image in a variety of directions. The most prominent directions found are then selected as primitive features. Starting from the selected directions with largest magnitudes a resultant is computed by including directional features in the image neighborhood. The process stops when inclusion of other points in the region makes the resultant direction change significantly from the initial one. This resultant is the axis of symmetry of that salient region. A rank is built showing in order the salient regions found in a scene. We report results on natural images showing a promising line of research for scene description and visual attention. © Springer-Verlag 2004.