Properties of basis functions generated by shift invariant sparse representations of natural images |
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Authors: | Wakako Hashimoto Koji Kurata |
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Institution: | (1) Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University, Machikaneyama 1-3, Toyonaka 560-8531, Osaka, Japan, JP |
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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.
Received: 3 November 1999 / Accepted in revised form: 17 February 2000 |
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Keywords: | |
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