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A novel biologically inspired local feature descriptor

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Abstract

Local feature descriptor is a fundamental representation for image patch which has been extensively used in many computer vision applications. In this paper, different from state-of-the-art features, a novel biologically inspired local descriptor (BILD) is proposed based on the visual information processing mechanism of ventral pathway in human brain. The local features used for constructing BILD are extracted by a two-layer network, which corresponds to the simple-to-complex cell hierarchy in the primary visual cortex (V1). It works in a similar way as the simple cell and complex cell do to get responses by applying the lateral inhibition from different orientations and operating an improved cortical pooling. To enhance the distinctiveness of BILD, we combine the local features from different orientations. Extensive evaluations have been performed for image matching and object recognition. Experimental results reveal that our proposed BILD outperforms many widely used descriptors such as SIFT and SURF, which demonstrate its efficiency for representing local regions.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (NSFC) under the Grant No. 61004111, No. 61273279 and No. 61273241.

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yun Zhang, Tian Tian, Jinwen Tian & Delie Ming

  2. China Ship Design and Research Center, Wuhan, 430064, China

    Junbin Gong

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  1. Yun Zhang
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  2. Tian Tian
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  4. Junbin Gong
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  5. Delie Ming
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Correspondence to Delie Ming.

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Zhang, Y., Tian, T., Tian, J. et al. A novel biologically inspired local feature descriptor. Biol Cybern 108, 275–290 (2014). https://doi.org/10.1007/s00422-013-0583-1

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