Salient object detection based on multi-scale region contrast

A novel visual saliency computing model is proposed based on multi-scale region contrast to perform more accurate detection on salient object. Firstly, the image is divided into different number of super-pixels based on multi-scale method, and the values of pixels in every super-pixel are averaged to create abstract image. Secondly, based on scarcity and aggregation, both of which are the characters of saliency, the color's saliency of super-pixel is computed in single scale. By averaging the salient images in every scale, the multi scale salient images are fused and the final visual salient image is obtained in the end. The simulation result shows that with 1000 random nature images in the MSRA Libraries, the model improves the precision ratio of salient object detection by 14.8% and F-Measure value by 9.2%, compared with current well-performed region contrast model. The model improves the adaptability of the size of salient objects, and reduces the disturbance of background. It performs better consistency and has better ability to recognize salient object in comparison with current algorithms.