In this paper, a semi-supervised dimensionality reduction algorithm named semi-supervised orthogonal discriminant projection (SSODP) was proposed and applied successfully to leaf classification. SSODP utilizes all labeled and unlabeled samples to construct the weight between any pairwise points.
Jul 11, 2015
The experimental results on the two public plant leaf databases demonstrate that SSODP is more effective in terms of plant leaf classification rate. References.
In this paper, we propose a novel semi-supervised method, called Semi-supervised discriminant projection (SSDP) dimension reduction algorithm for leaf ...
In this paper, we propose a novel semi-supervised method, called Semi-supervised discriminant projection (SSDP) dimension reduction algorithm for leaf ...
In this paper, a semi-supervised dimensional reduction algorithm named supervised orthogonal projection embedding (SSLDP) was proposed and applied successfully ...
Abstract. Plant leaf classification is important but very difficult, because the leaf images are irregular and nonlinear. In this paper, we propose a novel ...
In this paper, we propose a novel semi-supervised method, called Semi-supervised discriminant projection (SSDP) dimension reduction algorithm for leaf ...
Semi-Supervised Discriminant Projection for Plant Leaf Classification
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A novel semi-supervised method, called Semi- supervised discriminant projection (SSDP) dimension reduction algorithm for leaf recognition, which makes full ...
Plant leaf image classification based on supervised orthogonal locality preserving projections[J]. Transactions of the Chinese Society of Agricultural ...
In this paper, we apply 2DLPP to plant leaf classification. 2D-LDP can detect the intrinsic class-relationships between the leaf images by incorporating both ...