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LAP: a bio-inspired local image structure descriptor and its applications

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Abstract

Local structural information is crucially important for human visual system to perceive natural scenes. Recent years, a variety of local image structure description methods have been proposed for the artificial modeling of visual perception. Although existing local image structure descriptors have shown successful performances, one general limitation is their numerical instability caused by ignoring the information of the spatial correlation of local orientation. In this paper, we propose a local image structure descriptor by modeling the anisotropic mechanism in the primary visual cortex. In particular, the pixel-wise anisotropy values of a given image are calculated by pseudo-Wigner-Ville distribution (PWVD) and Rényi entropy. Then the excitatory/inhibitory interactions among visual neurons in the local receptive field are modeled by measuring the similarities between their anisotropies. By mapping visual neurons to image pixels, the correlation between a central pixel and its local neighbors can be represented by a binary pattern which is named as local anisotropic pattern (LAP). Experimental results on texture classification verified that the proposed LAP has satisfactory texture classification accuracy, rotation invariance, and noise robustness; experimental results on no-reference image quality assessment demonstrated that the proposed LAP achieves state-of-the-art performance in objective evaluation of the perceptual quality of natural image, and this reflects that LAP can accurately represent the degradation of local image structure.

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Acknowledgments

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. They would also like to thank the members at the Institute of Circuits and Systems, School of Information Science and Engineering, Lanzhou University, for valuable discussions. This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 61175012 and the Fundamental Research Funds for the Central Universities of China under Grant lzujbky-2015-196.

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  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Songlin Du, Yaping Yan & Yide Ma

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  1. Songlin Du
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Correspondence to Songlin Du.

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Du, S., Yan, Y. & Ma, Y. LAP: a bio-inspired local image structure descriptor and its applications. Multimed Tools Appl 76, 13973–13993 (2017). https://doi.org/10.1007/s11042-016-3779-2

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