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Machine Vision and Applications
Published in: Machine Vision and Applications 6/2018

06-04-2018 | Special Issue Paper

Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds

Authors: Limiao Deng, Yanjiang Wang, Baodi Liu, Weifeng Liu, Yujuan Qi

Published in: Machine Vision and Applications | Issue 6/2018

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Abstract

Hierarchical MAX model (HMAX) is a bio-inspired model mimicking the visual information processing of visual cortex. However, the visual processing of lower level, such as retina and lateral geniculate nucleus (LGN), is not concerned, and the properties of higher-level neurons are not sufficiently specified. Given that, we develop an extended HMAX model, denoted as E-HMAX, by the following biologically plausible ways. First, contrast normalization is conducted on the input image to simulate the processing of human retina and LGN. Second, log-polar Gabor (GLoP) filters are used to simulate the properties of V1 simple cells instead of Gabor filters. Then, sparse coding on multi-manifolds is modeled to compute the V4 simple cell response instead of Euclidean distance. Meanwhile, a template learning method based on dictionary learning on multi-manifolds is proposed to select informative templates during template learning stage. Experimental results demonstrate that the proposed model has greatly outperformed the standard HMAX model. It is also comparable to some state-of-the-art approaches such as EBIM and OGHM-HMAX.
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Metadata
Title
Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds
Authors
Limiao Deng
Yanjiang Wang
Baodi Liu
Weifeng Liu
Yujuan Qi
Publication date
06-04-2018
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 6/2018
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-018-0928-9

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