A model of saliency-based visual attention for rapid scene analysisLaurent Itti, Christof Koch, Ernst Niebur|IEEE Transactions on Pattern Analysis and Machine Intelligence|1998 A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.
Graph-Based Visual SaliencyA 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 \nchannels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible \ninsofar as it is naturally parallelized. This model powerfully predicts human fixations on 749 variations of 108 natural images, achieving 98% of the ROC area \nof a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.