Modeling Manifold Ways of Scene Perception

In this paper, under the efficient coding theory we propose a computational model to explore the intrinsic dimensionality of scene perception. This model is hierarchically constructed according to the information pathway of visual cortex: By pooling together the activity of local low-level feature detectors across a large regions of the visual fields, we build the population feature representation as the statistical summary of the input image. Then, a large amount of population feature representations of scene images are embedded unsupervisedly into a low-dimensional space called perceptual manifold. Further analysis on the perceptual manifold reveals the topographic properties that 1) scene images which share similar perceptual similarity stay nearby in the manifold space, and 2) dimensions of the space could describe the perceptual continuous changes in the spatial layout of scenes, representing the degree of naturalness, openness, etc. Moreover, scene classification task is implemented to validate the topographic properties of the perceptual manifold space.