Label propagation based supervised locality projection analysis for plant leaf classification

Authors:
Shanwen Zhang;Yingke Lei;Tianbao Dong;Xiao-Ping Zhang
Affiliations:
Department of Engineering and Technology, Xijing University, Xi'an 710123, China and Department of Electrical and Computer Engineering, Ryerson University, Toronto, M5B 2K3, Canada;Electronic Engineering Institute, Hefei, Anhui 230027, China;Electronic Engineering Institute, Hefei, Anhui 230027, China;Department of Electrical and Computer Engineering, Ryerson University, Toronto, M5B 2K3, Canada
Venue:
Pattern Recognition
Year:
2013

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Abstract

The label propagation has the benefits of nearly-linear running time and easy implementation. In this paper, we make use of the label propagation to propose a new weight measure, and present a supervised locality projection analysis (SLPA) method for plant leaf classification. Firstly, we apply Warshall algorithm to label propagation and get the label matrix, then incorporate it into the weight, which has a clear physical meaning. Secondly, multi-class data points in high-dimensional space are to be pulled or pushed by discriminant neighbors to form an optimum projecting to low dimensionality. Finally, the experimental results on two plant leaf databases show that the proposed method is quite effective and feasible.