Natural Image Statistics and Eecient Coding

Natural images contain characteristic statistical regularities that set them apart from purely random images. Understanding what these regularities are can enable natural images to be coded more eeciently. In this paper, we describe some of the forms of structure that are contained in natural images, and we show how these are related to the response properties of neurons at early stages of the visual system. Many of the important forms of structure require higher-order (i.e., more than linear, pairwise) statistics to characterize, which makes models based on linear Hebbian learning, or principal components analysis, inappropriate for nding eecient codes for natural images. We suggest that a good objective for an eecient coding of natural scenes is to maximize the sparseness of the representation, and we show that a network that learns sparse codes of natural scenes succeeds in developing localized, oriented, bandpass receptive elds similar to those in the primate striate cortex.

[1]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[2]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[3]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[4]  A. Parker,et al.  Two-dimensional spatial structure of receptive fields in monkey striate cortex. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[5]  Ralph Linsker,et al.  Self-organization in a perceptual network , 1988, Computer.

[6]  Terence D. Sanger,et al.  An Optimality Principle for Unsupervised Learning , 1988, NIPS.

[7]  Leslie S. Smith,et al.  The principal components of natural images , 1992 .

[8]  D. Ruderman.  The statistics of natural images , 1994 .

[9]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[10]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[11]  I. Ohzawa,et al.  Receptive-field dynamics in the central visual pathways , 1995, Trends in Neurosciences.

[12]  David J. Field,et al.  Sparse Coding of Natural Images Produces Localized, Oriented, Bandpass Receptive Fields , 1995 .

[13]  T J Sejnowski,et al.  Learning the higher-order structure of a natural sound. , 1996, Network.

[14]  R W Prager,et al.  Development of low entropy coding in a recurrent network. , 1996, Network.