Saliency detection in computer rendered images based on object-level contrast

In this work, we propose a novel graphic saliency detection method to detect visually salient objects in images rendered from 3D geometry models. Different from existing graphic saliency detection methods, which estimate saliency based on pixel-level contrast, the proposed method detects salient objects by computing object-level contrast. Given a rendered image, the proposed method first extracts dominant colors from each object, and represents each object with a dominant color descriptor (DCD). Saliency of each object is then calculated by measuring the contrast between the DCD of the object and the DCDs of its surrounding objects. We also design a new iterative suppression operator to enhance the saliency result. Compared with existing graphic saliency detection methods, the proposed method can obtain much better performance in salient object detection. We further apply the proposed method to selective image rendering and achieve better performance over the relevant existing algorithm.