Heuristic Discrimination Cotton Ripeness Using Hybrid Filter and Wrapper

Raw ripeness discrimination is a pivotal technology of machine vision system of cotton picking robot. The dimensionality curse exists in spatial and frequency feature sets describing morphology structure and boundary contour of raw cotton, and their feature selection problem is an NP hard problem. A solution algorithm is proposed using floating search by filter and stopping search by wrapper based on cross validation. The optimal l feature subset (l=1,2,3,…) is selected on training set using filter with an assessing function of class-separability measured value and heuristic criterion including optimal scalar feature combination and floating search. A model is established on training set using the optimal l feature subset by wrapper with an assessing function of the error rate of Bayes-classifier. The optimal feature subset is with a capacity of 6 at the minimum value of average error rate of the model on validation set, and the average classification rate of which on prediction sets is 87.61%. The algorithm has been validated on 40 databases involved in correlative research work, and experimental results show that the algorithm has good classification performance and higher execution efficiency on 29 datasets.