Combining saliency estimation methods

Abstract

We address the task of image saliency estimation through proper recombination of existing methods in the state of the art. We define a general scheme, which we then specialize to perform dataset-specific and image-specific recombination, based on either linear weight regression, or method selection. The advantage of this approach lies in the possibility of exploiting the different strengths of existing methods. Experiments are conducted with both deep learning and hand-crafted methods on a widely used dataset, using standard evaluation measures. The proposed recombination strategy allows us to improve upon the state of the art, by exploiting a linear combination of the saliency maps produced by existing methods. We also show that image-specific combination and selection of saliency maps is limited by the apparent lack of relevant information intrinsic in the image itself.