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.