In many of the existing alpha matting implementations, an intermediate representation called a trimap needs to be created manually. To automate the process, we propose a generic neural network for trimap generation based on saliency map detection. Our model multi-modally learns a saliency map and a trimap simultaneously. Because of this structure, the network focuses on reducing the error of the trimap especially within the areas with high salience. We used both the saliency map detection dataset and the alpha matting dataset to achieve accuracy in extracting subjects from natural images and generating trimaps. Experiments showed that our model could generate trimaps that are almost identical to manually generated ones. The method can also be easily combined with existing alpha matting algorithms.