The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Natural image statistics and efficient coding
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.
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Cites
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Receptive fields and functional architecture of monkey striate cortex
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“the spatial frequency response is generally bandpass with bandwidths in the range of 1‐2 octaves [6, 12, 13 , 16].”
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Research works printed but not linked: 6
- 10.1088/0954-898X_7_2_005 · DOI
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Cited by
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Self-citationSparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Self-citationThe “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 2,201
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
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Metadata
- Authors
- Olshausen, B. A., & Field, D. J.
- Venue
- Network: Computation in Neural Systems
- Publisher
- Informa UK Limited
- Published
- 1996-01
- Volume
- 7
- Issue
- 2
- Pages
- 333-339
- ISSN
- 0954-898X (ISSN-L)
- 1361-6536
- OpenAlex
- W2145012779
- Cited by
- 561
- References
- 17
- FWCI
- 9.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- Olshausen, B. A., & Field, D. J. (1996). Natural image statistics and efficient coding. Network: Computation in Neural Systems. https://doi.org/10.1088/0954-898x_7_2_014
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
