Scale Saliency: a novel approach to salient feature and scale selection

This paper presents an overview of the Scale Saliency algorithm introduced in (Kadir and Brady, 2001). Scale Saliency is a novel method for measuring the saliency of image regions and selecting optimal scales for their analysis. The model underlying the algorithm deems image regions salient if they are simultaneously unpredictable in some feature-space and over scale. The algorithm possesses a number of attractive properties: invariance to planar rotation, scaling, intensity shifts and translation; robustness to noise, changes in viewpoint, and intensity scalings. Moreover, the approach offers a more general model of feature saliency compared with conventional ones, such as those based on kernel convolution, for example wavelet analysis, since such techniques define saliency and scale only with respect to a particular set of basis morphologies. Finally, we present a generalised version of the original algorithm which is invariant to affine transformations.