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Lower-dimensional intrinsic structural representation of leaf images and plant recognition

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Abstract

This paper proposes a statistical representation called “Eigenleaves” for leaf images dependent on their natural structure. The proposed presentation possesses several improvements over traditional image representation methods as a feature vector, such as being simple and fully automatic. We aim toward representing an application of the eigenvector that finds the geometrical structure and correlated information. The method automates feature extraction without explicitly specifying the most dominating attribute and deals with issues raised when working with high-dimensional images. Our finding confirms that the proposed representation is efficient and significant. It is comparable to deep learning approaches that require lots of computational costs and a high number of training samples to find the hidden structure of leaves that need to be defined at an earlier stage. Encoded leaf images are stored for futuristic applications with lesser dimensionality and essential information.

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Acknowledgements

We acknowledge University Grant Commission, India, to provide financial assistance to one of the authors Ms. Neha Goyal under the scheme NET-JRF.

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Authors and Affiliations

  1. NIT Kurukshetra, Kurukshetra, India

    Neha Goyal & Kapil Gupta

  2. NIT Kurukshetra, Uttarakhand, India

    Nitin Kumar

Authors
  1. Neha Goyal
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  2. Nitin Kumar
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  3. Kapil Gupta
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Correspondence to Neha Goyal.

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Goyal, N., Kumar, N. & Gupta, K. Lower-dimensional intrinsic structural representation of leaf images and plant recognition. SIViP 16, 203–210 (2022). https://doi.org/10.1007/s11760-021-01983-6

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  • DOI: https://doi.org/10.1007/s11760-021-01983-6

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