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.

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Hashimoto W,Kurata K

doi

10.1007/s004220000149

keywords:

subject

Has Abstract

pub_date

2000-08-01 00:00:00

pages

111-8

issue

2

eissn

0340-1200

issn

1432-0770

journal_volume

83

pub_type

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