Sparse coding with an overcomplete basis set: A strategy employed by V1?

@article{Olshausen1997SparseCW,
  title={Sparse coding with an overcomplete basis set: A strategy employed by V1?},
  author={Bruno A. Olshausen and David J. Field},
  journal={Vision Research},
  year={1997},
  volume={37},
  pages={3311-3325}
}

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References

SHOWING 1-10 OF 71 REFERENCES

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

It is shown that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex.

What Is the Goal of Sensory Coding?

  • D. Field
  • Computer Science
    Neural Computation
  • 1994
It is proposed that compact coding schemes are insufficient to account for the receptive field properties of cells in the mammalian visual pathway and suggested that natural scenes, to a first approximation, can be considered as a sum of self-similar local functions (the inverse of a wavelet).

Entropy reduction and decorrelation in visual coding by oriented neural receptive fields

  • J. Daugman
  • Computer Science, Biology
    IEEE Transactions on Biomedical Engineering
  • 1989
The present image coding simulations, based on quantitative neurobiological data about the code primitives, provide measures of the bit-rate efficiency of such oriented, quadrature, neural codes.

Natural Image Statistics and Eecient Coding

It is suggested that a good objective for an eecient coding of natural Scenes is to maximize the sparseness of the representation, and it is shown 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.

Relations between the statistics of natural images and the response properties of cortical cells.

  • D. Field
  • Computer Science
    Journal of the Optical Society of America. A, Optics and image science
  • 1987
The results obtained with six natural images suggest that the orientation and the spatial-frequency tuning of mammalian simple cells are well suited for coding the information in such images if the goal of the code is to convert higher-order redundancy into first- order redundancy.

Finding compact and sparse-distributed representations of visual images

An artificial neural network which self-organizes on the basis of simple Hebbian learning and negative feedback of activation is introduced and it is shown that it is capable both of forming compact codings of data distributions and of identifying filters most sensitive to sparse-distributed codes.

Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory

The results suggest that the early visual pathway is well adapted for efficient coding of information in the natural visual environment, in agreement with the prediction of the computational theory.

Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex

A hierarchical network model of visual recognition that explains experimental observations regarding neural responses in both free viewing and fixating conditions by using a form of the extended Kalman filter as given by the minimum description length (MDL) principle is described.

What Does the Retina Know about Natural Scenes?

It is argued that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex, as suggested by Barlow.

A Model of the Spatial-Frequency Organization in Primate Striate Cortex

Here, a model of the spatial frequency organization that would be required in primate V1 in order to form a complete representation of spatial information provided by the optic nerve is presented.
...