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- W2381645336 abstract "Objective To study an innovative method of dimension reduction of image feature by means of C4.5 decision tree classification algorithm.Methods Firstly,such operations as image filtering,gray threshold and corrosion arithmetic were applied to segment Xinjiang herb image for obtaining the region of interest(ROI)of herb shape.Secondly,14 feature components,including herbal image shape FourierMellin moments,Hu moments and the herbal image main color histogram features were extracted.Finally,C4.5 decision tree classification algorithm was adopted and 5 optimized components were identified as the image mixed shape-color features for the herbal image classification.Results The classification accuracy rate of this method was proved to be 88.55%;the average precision rate in the top 60 images retrieval with the optimized mixed shape-color feature was up to 89.31%.Conclusion The optimization and dimension reduction of image feature could be realized by means of C4.5 decision tree classification algorithm to provide a reference for the further optimization of image features." @default.
- W2381645336 created "2016-06-24" @default.
- W2381645336 creator A5021549120 @default.
- W2381645336 date "2015-01-01" @default.
- W2381645336 modified "2023-09-26" @default.
- W2381645336 title "Optimization on the features of herbal shapes by C4.5 decision tree" @default.
- W2381645336 hasPublicationYear "2015" @default.
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