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

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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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Acknowledgements

The paper is funded by the National Natural Science Foundation of China (No. 61671480) and the Natural Science Foundation of Shandong Province (Nos. ZR2017MF069, ZR2018MF017).

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Authors and Affiliations

  1. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China

    Limiao Deng, Yanjiang Wang, Baodi Liu, Weifeng Liu & Yujuan Qi

  2. College of Science and Information, Qingdao Agricultural University, Qingdao, China

    Limiao Deng

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  1. Limiao Deng
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  2. Yanjiang Wang
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Correspondence to Yanjiang Wang.

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Deng, L., Wang, Y., Liu, B. et al. Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds. Machine Vision and Applications 29, 965–977 (2018). https://doi.org/10.1007/s00138-018-0928-9

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