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Discriminative transform of receptive field patterns for feature representation

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Abstract

As reported by many neurophysiological researches, the receptive field is a basic and significant component in the human visual system. It has various kinds of properties such as orientation-selectivity, correlativity, etc. Motivated by these structural and functional properties, we propose in this paper a novel local image descriptor namely the Discriminative Transform of Receptive Field (DTRF). Specifically, around each sample pixel in the interest region, we define a low-level feature structure called Receptive Field Patterns (RFP) which is further divided into two components: the RFP-Center and RFP-Surround. Then, the local features are extracted based on Local Annular Discrete Cosine Transform (LADCT). At the descriptor construction stage, these features are pooled spatially to mimic the correlative property of receptive field. Image matching and classification experiments on four standard data set demonstrate that the proposed descriptor outperforms the state-of-the-art methods under various types of image transformations such as rotation and scaling changes, viewpoint changes, image blurring, JPEG compression, illumination changes, and image noise.

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Acknowledgment

This work is partially supported by the National Natural Science Foundation of China(Grant 61073094 and U1233119).

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Authors and Affiliations

  1. School of Computer Science & Technology, Huazhong University of Science & Technology, Wuhan, 430074, China

    Yucheng Shu, Tianjiang Wang & Guangpu Shao

  2. JiangSu University of Science and Technogly, No.2 Mengxi Road, Zhenjiang, Jiangsu, China

    Chunlong Hu

Authors
  1. Yucheng Shu
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  2. Tianjiang Wang
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  3. Guangpu Shao
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  4. Chunlong Hu
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Correspondence to Yucheng Shu.

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Shu, Y., Wang, T., Shao, G. et al. Discriminative transform of receptive field patterns for feature representation. Multimed Tools Appl 75, 7495–7517 (2016). https://doi.org/10.1007/s11042-015-2673-7

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  • DOI: https://doi.org/10.1007/s11042-015-2673-7

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