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Discriminant WSRC for Large-Scale Plant Species Recognition.

Shanwen Zhang1,2, Chuanlei Zhang1, Yihai Zhu2

  • 1Department of Information Engineering, Xijing University, Xi'an 710123, China.

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Discriminant Weighted Sparse Representation Classification (DWSRC) speeds up plant species recognition. This method uses subdictionaries for faster, more accurate, and interpretable large-scale leaf classification.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Botanical Informatics

Background:

  • Sparse Representation based Classification (SRC) and Weighted SRC (WSRC) face computational challenges in solving global sparse representation problems.
  • Accurate and efficient plant species recognition is crucial for large-scale botanical datasets.

Purpose of the Study:

  • To propose a Discriminant Weighted Sparse Representation Classification (DWSRC) method for efficient large-scale plant species recognition.
  • To reduce the computational complexity associated with traditional SRC and WSRC methods.
  • To adapt the classification model to newly added leaf species without extensive retraining.

Main Methods:

  • DWSRC employs a two-stage approach: constructing subdictionaries from similar classes and selecting the most relevant subdictionary for a test sample.
  • Sparse representation is computed using the selected subdictionary, and classification is achieved by minimizing reconstruction error.
  • The method utilizes training samples of selected similar classes as base elements for subdictionaries, differing from generic overcomplete dictionaries.

Main Results:

  • Experimental results on the ICL plant leaf database demonstrate DWSRC's low computational complexity.
  • The proposed method achieves a high recognition rate for plant species.
  • DWSRC offers clear interpretability of the classification process.

Conclusions:

  • DWSRC significantly reduces the complexity of solving sparse representation problems in classification tasks.
  • The method is effective for large-scale plant species recognition, offering both speed and accuracy.
  • DWSRC's adaptability to new species enhances its practical applicability in botanical informatics.