Multi-scale contrast-based saliency enhancement for salient object detection

To achieve more complete and more uniformly highlighted salient object regions, this study presents a computational saliency enhancement model that incorporates the properties of multi-scale and logarithmic response into the local and global contrasts. A distinct feature of the authors model is a novel saliency enhancement operator. This operator can effectively enhance the saliency of object interior regions while simultaneously reducing blur on object boundaries caused by multiple scales. Their model is a general one that can make flexible tradeoffs between precision and recall. Detailed comparisons with 12 state-of-the-art methods show that their method can obtain satisfactory salient object regions that are closer to the human-labelled results. In addition, their method provides superior results in precision-recall, F-measure and mean absolute error.