Saliency detection based on adaptive DoG and distance transform

Abstract

A novel computational model for detecting salient regions in color images is proposed, based on adaptive difference of Gaussian (DoG) filtering and distance transform. In our method, we first transform an image into the frequency domain, and perform adaptive DoG filtering, whose parameters are determined by the energy spectrum of the image. Then, the edge information is extracted from the DoG filtering output, and the distance transform is applied to the edge map. Finally, the Gaussian pyramids are used to enhance the distance transform performance. Our proposed method achieves spectral domain filtering as well as spatial domain edge extraction, thus exploiting the benefits from both the spatial domain and the spectral domain for saliency detection. We compare our proposed method with five existing saliency detection methods in terms of precision, recall, and F-measure. Experiments on the MSRA dataset show the outperformance of the proposed method over those saliency algorithms.