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Plant leaf recognition using texture features and semi-supervised spherical K-means clustering

Alamoudi, S., Hong, X. and Wei, H. (2020) Plant leaf recognition using texture features and semi-supervised spherical K-means clustering. In: 2020 International Joint Conference on Neural Networks (IJCNN), 19-24 July 2020, Glasgow, United Kingdom (virtual), https://doi.org/10.1109/IJCNN48605.2020.9207386.

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To link to this item DOI: 10.1109/IJCNN48605.2020.9207386

Abstract/Summary

Automatic plant leave recognition using digital images and machine learning techniques is an important task. The disadvantage of supervised learning techniques is that they are limited to learn from labelled datasets which are often expensive to obtain. In this paper, a novel decision fusion framework is proposed by combining semi-supervised clustering with the well known image features analysis methods in computer vision. Initially the leave image features are generated by applying the Grey Level Co-occurrence Matrix analysis to the processed leave images transformed by Gabor or Laplacian of Gaussian filters. Then an on-line spherical k-means clustering technique, guided by a minimum number of labelled leaves, is used to train the base classifiers. The final decision of classification is produced by selecting classifier which produces the max-cosine value amongst the baseline classifiers. Comparative experiments have been carried out to demonstrate that proposed approaches are suited for automatic leave type recognition.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:93980

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