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Semi-supervised orthogonal discriminant projection for plant leaf classification

ABSTRACT

Abstract

  Plant classification based on the leaf images is an important and tough task. For leaf classification problem, in this paper, a new weight measure is presented, and then a dimensional reduction algorithm, named semi-supervised orthogonal discriminant projection (SSODP), is proposed. SSODP makes full use of both the labeled and unlabeled data to construct the weight by incorporating the reliability information, the local neighborhood structure and the class information of the data. The experimental results on the two public plant leaf databases demonstrate that SSODP is more effective in terms of plant leaf classification rate.Abstract
  Plant classification based on the leaf images is an important and tough task. For leaf classification problem, in this paper, a new weight measure is presented, and then a dimensional reduction algorithm, named semi-supervised orthogonal discriminant projection (SSODP), is proposed. SSODP makes full use of both the labeled and unlabeled data to construct the weight by incorporating the reliability information, the local neighborhood structure and the class information of the data. The experimental results on the two public plant leaf databases demonstrate that SSODP is more effective in terms of plant leaf classification rate.