Abstract

In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to a?ne invariance of the method introduced by Kadir and Brady [10]. The de- tector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale. The detector has significantly difierent properties to operators based on kernel convolution, and we examine three aspects of its behaviour: invari- ance to viewpoint change; insensitivity to image perturbations; and re- peatability under intra-class variation. Previous work has, on the whole, concentrated on viewpoint invariance. A second contribution of this pa- per is to propose a performance test for evaluating the two other aspects. We compare the performance of the saliency detector to other stan- dard detectors including an a?ne invariance interest point detector. It is demonstrated that the saliency detector has comparable viewpoint invariance performance, but superior insensitivity to perturbations and intra-class variation performance for images of certain ob ject classes.