Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 1996 May;7(2):333-9.
doi: 10.1088/0954-898X/7/2/014.

Natural Image Statistics and Efficient Coding

Affiliations

Natural Image Statistics and Efficient Coding

B A Olshausen et al. Network. .

Abstract

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 efficiently. 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 finding efficient codes for natural images. We suggest that a good objective for an efficient 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 fields similar to those in the mammalian striate cortex.

Similar articles

See all similar articles

Cited by 61 articles

See all "Cited by" articles

LinkOut - more resources

Feedback