gbvs_nips 基于图的显著性。Graph-Based Visual Saliency。是发表在2006年NIPS上的论文。(A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: first forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insorfar as it is naturally parallelized. This model powerfully predicts human fixations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithas of Itti & Koch ([2], [3], [4]) achieve only 84%.)