Determining visual saliency is one of the fundamental problems in computer vision as the saliency not only identifies the most informative parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. In this paper, we propose a novel saliency object detection method that combines a shape-preserving saliency prediction driven by a convolutional neural network with the mid and low-level region preserving image information. Our model learns a saliency shape dictionary, which is subsequently used to train a CNN to predict the salient class of a target region and estimate the full but coarse saliency map of the target image. The map is then refined using image specific low-to-mid level information. Performance evaluation on popular benchmark datasets shows that the proposed method outperforms existing state-of-the-art methods in saliency detection.