Properties of basis functions generated by shift invariant sparse representations of natural images

Abstract. The idea that a sparse representation is the computational principle of visual systems has been supported by Olshausen and Field [Nature (1996) 381: 607–609] and many other studies. On the other hand neurons in the inferotemporal cortex respond to moderately complex features called icon alphabets, and such neurons respond invariantly to the stimulus position. To incorporate this property into sparse representation, an algorithm is proposed that trains basis functions using sparse representations with shift invariance. Shift invariance means that basis functions are allowed to move on image data and that coefficients are equipped with shift invariance. The algorithm is applied to natural images. It is ascertained that moderately complex graphical features emerge that are not as simple as Gabor filters and not as complex as real objects. Shift invariance and moderately complex features correspond to the property of icon alphabets. The results show that there is another connection between visual information processing and sparse representations.

[1]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[3]  B. Olshausen.  Learning linear, sparse, factorial codes , 1996 .

[4]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[5]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[6]  Terrence J. Sejnowski,et al.  Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations , 1998, NIPS.

[7]  Terrence J. Sejnowski,et al.  Blind separation and blind deconvolution: an information-theoretic approach , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[8]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[9]  Rajesh P. N. Rao,et al.  Learning Lie Groups for Invariant Visual Perception , 1998, NIPS.

[10]  A. Borst.  Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[11]  Bruno A. Olshausen,et al.  PROBABILISTIC FRAMEWORK FOR THE ADAPTATION AND COMPARISON OF IMAGE CODES , 1999 .

[12]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[13]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[14]  Rajesh P. N. Rao,et al.  Development of localized oriented receptive fields by learning a translation-invariant code for natural images. , 1998, Network.

[15]  Keiji Tanaka,et al.  Representation of Visual Features of Objects in the Inferotemporal Cortex , 1996, Neural Networks.

[16]  Minami Ito,et al.  Columns for visual features of objects in monkey inferotemporal cortex , 1992, Nature.

[17]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.