Class BOWKMeansTrainer


  • public class BOWKMeansTrainer
    extends BOWTrainer
    kmeans -based class to train visual vocabulary using the *bag of visual words* approach. :
    • Constructor Detail

      • BOWKMeansTrainer

        protected BOWKMeansTrainer​(long addr)
      • BOWKMeansTrainer

        public BOWKMeansTrainer​(int clusterCount,
                                TermCriteria termcrit,
                                int attempts,
                                int flags)
        The constructor. SEE: cv::kmeans
        Parameters:
        clusterCount - automatically generated
        termcrit - automatically generated
        attempts - automatically generated
        flags - automatically generated
      • BOWKMeansTrainer

        public BOWKMeansTrainer​(int clusterCount,
                                TermCriteria termcrit,
                                int attempts)
        The constructor. SEE: cv::kmeans
        Parameters:
        clusterCount - automatically generated
        termcrit - automatically generated
        attempts - automatically generated
      • BOWKMeansTrainer

        public BOWKMeansTrainer​(int clusterCount,
                                TermCriteria termcrit)
        The constructor. SEE: cv::kmeans
        Parameters:
        clusterCount - automatically generated
        termcrit - automatically generated
      • BOWKMeansTrainer

        public BOWKMeansTrainer​(int clusterCount)
        The constructor. SEE: cv::kmeans
        Parameters:
        clusterCount - automatically generated
    • Method Detail

      • cluster

        public Mat cluster​(Mat descriptors)
        Description copied from class: BOWTrainer
        Clusters train descriptors.
        Overrides:
        cluster in class BOWTrainer
        Parameters:
        descriptors - Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set. The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.
        Returns:
        automatically generated