The prominent features of the salient region are determined by focusing on the regional boundary and the object's edge pixels. Further, the uniqueness of the salient global color is used to calculate global features. Finally, the salient region is obtained using the convolutional neural network (CNN) model based on the regional and global salient features. Adopting a circular structure network is critical to eliminate the noise characteristics by referring to the surrounding environment information for multiple times. The proposed algorithm is tested using the image libraries of MSRA and ECSSD and it is found that its harmonic mean and average error associated with the average precision and recall are better than those of the current popular algorithms.