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In th is paper, a p lan t leaf image set iden tifica tion approach was proposed based on non-linear reconstruction models. T h is approach in itia lizes the parameters o f m odel by pe rfo rm ing unsupervised p re -tra in ing using Gaussian re­ s tric ted Boltzm ann m ach ines(G R B M s). T he n , the p re -in itia lized model is separately tra ined fo r images o f each p lan t set and class-specific models are learnt. A t las t, based on the m in im um reconstruction e rro r fro m the learn t class-specific m o d e ls ,m a jo rity vo ting s trategy is used fo r classification. Besides, in o rder to avoid occurring deform ation du ring the image scaled, th is paper norm alized p lan t image by image preprocessing and a m ethod o f feature extrac tion was used based on k-means. T he experim ental resu lts show th a t th is approach can accurately c lassify the class o f p lan t image set.

基于非线性重构模型的植物叶片图像集分类方法 (Plant Leaf Image Set Classification Approach Based on Non-linear Reconstruction Models).