Top-down guided eye movements: peripheral model

Eye movements are an important aspect of human visual behavior. The temporal and space-variant nature of sampling a visual scenes requires frequent attentional gaze shifts, saccades, to fixate onto different parts of an image. Fixations are often directed towards the most informative regions in the visual scene. We introduce a model and its simulation that can select such regions based on prior knowledge of similar scenes. Having representations of scene categories as probabilistic combination of hypothetical objects, i.e., prototypical regions with certain properties, it is possible to assess the likely contribution of each image region to the successive recognition process. The regions are obtained by segmenting low-resolution images using the normalized cut algorithm. Based on low-level features, such as average color, size, position, regions are clustered into a small set of hypothetical objects. Using conditions probabilities for each object given the scene category, the model can then predict the informative value of the corresponding region and initiate a sequential spatial information-gathering algorithm analogous to an eye movement saccade to a new fixation. The article demonstrates how the initial hypothesis determines the next region of interest to visit and how these scene hypotheses are affected by sequentially visiting each new image region.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.