Influence of Spatial Resolution on State-of-the-Art Saliency Models

Visual attention has been widely investigated and applied in recent decades. Various computation models have been proposed to modeling visual attention, but most researches are conducted under the assumption that given images have few limited spatial resolutions. Spatial resolution is an important feature of image. Image resolution may have some influence on visual attention, and it may also affect the effectiveness of visual attention models. The influence of spatial resolution on saliency models has not been systematically investigated before. In this paper, we discuss two problems related to image resolution and saliency: (1) Most saliency models contain down-sampling function which changes the resolution of original images to lower the computational complexity and keep the formalization of the algorithm. In the first part, we discuss the influence of the down-sampling ratio on the effectiveness of classic saliency models. (2) In the second part, we investigate the effectiveness of saliency models in images of various resolutions. A dataset which provides images and corresponding eye movement data in various spatial resolutions is used in this part. We apply the default rescaling parameters and keep them unchanged. Then we analyze the performance of classic models on 8 resolution levels. In summary, we systematically investigate and analyze problems concerning spatial resolution in the research of saliency modeling. The results of this work can provide a guide to the use of classic models in images of different resolutions and they are helpful to computational complexity optimization.