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Structural information processing in early vision using Hebbian-based mean shift

This paper presents a biologically plausible model for the structural information processing in early vision. Our investigation on the frequency spectrum of natural images filtered by the retina shows that the DC component containing much redundancy information and the high frequency components containing much noisy information are reduced, while the middle and low frequency components containing much structural information are enhanced. Simple cells in the primary visual cortex (V1) extract structural primitives from the filtered signals resulting in the emergence of diverse receptive field shapes. We name these structural primitives as structors, and study the neural mechanisms responsible for this diversity of V1 simple cell receptive field shapes. Sparse coding with the L0-norm constraint is reexamined which suggests that the local structure of natural images is determined by few structors regardless of their coefficients. We perform an analysis on the spatial distribution of the input signal and prove that signals in the neighborhood of a special structor has a star shape and peaks at the structor. That is, the structors are the modes of the probability density function of the input signal, and learning the structors can be interpreted as mode detection. Mean sift method is applied to detect modes, and the updating rule for the mean shift appears to be Hebbian. We propose the Hebbian-based mean shift to simulate the emergence of the diversity of simple cell receptive field shapes. The simulation results demonstrate the robustness of the proposed algorithm in producing both Gabor-like and blob-like structors.