Task driven saliency detection for image retargeting

Importance map plays a crucial role in the seam-carving process, a well known image retargeting method. Usually a saliency map (with central bias or single Gaussian priority) is chosen as the importance map. However, we find that direct use of saliency map for seam-carving may cause some unsatisfactory visual effects such as trivial solution or distortion. In this paper, we analyze the reasons and give improved importance map by fusing a multi-Gaussian saliency map (MG-SM) and a revised slant edge saliency map (SE-SM). Specifically for the multi-Gaussian saliency map, we assume that the priori saliency distribution of an image is multi-Gaussian centered at some object centers rather than single Gaussian. Super pixels and sparse representation are used to measure the saliency. For the revised slant edge saliency map, we use wavelet transform to find the slant edges and design to carve seams on the slant edges uniformly. The present method has been extensively tested and more satisfactory experimental results, especially for the slant edge distortion, are obtained than the other methods compared.