opencv/modules/contrib/src/bowmsctrainer.cpp

/*M///////////////////////////////////////////////////////////////////////////////////////
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// This file originates from the openFABMAP project:
// [http://code.google.com/p/openfabmap/]
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// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
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// Original Algorithm by Mark Cummins and Paul Newman:
// [http://ijr.sagepub.com/content/27/6/647.short]
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
// [http://ijr.sagepub.com/content/30/9/1100.abstract]
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// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
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#include "precomp.hpp"
#include "opencv2/contrib/openfabmap.hpp"

namespace cv {

namespace of2 {

BOWMSCTrainer::BOWMSCTrainer(double _clusterSize) :
    clusterSize(_clusterSize) {
}

BOWMSCTrainer::~BOWMSCTrainer() {
}

Mat BOWMSCTrainer::cluster() const {
    CV_Assert(!descriptors.empty());
    int descCount = 0;
    for(size_t i = 0; i < descriptors.size(); i++)
    descCount += descriptors[i].rows;

    Mat mergedDescriptors(descCount, descriptors[0].cols,
        descriptors[0].type());
    for(size_t i = 0, start = 0; i < descriptors.size(); i++)
    {
        Mat submut = mergedDescriptors.rowRange((int)start,
            (int)(start + descriptors[i].rows));
        descriptors[i].copyTo(submut);
        start += descriptors[i].rows;
    }
    return cluster(mergedDescriptors);
}

Mat BOWMSCTrainer::cluster(const Mat& _descriptors) const {

    CV_Assert(!_descriptors.empty());

    // TODO: sort the descriptors before clustering.


    Mat icovar = Mat::eye(_descriptors.cols,_descriptors.cols,_descriptors.type());

    std::vector<Mat> initialCentres;
    initialCentres.push_back(_descriptors.row(0));
    for (int i = 1; i < _descriptors.rows; i++) {
        double minDist = DBL_MAX;
        for (size_t j = 0; j < initialCentres.size(); j++) {
            minDist = std::min(minDist,
                cv::Mahalanobis(_descriptors.row(i),initialCentres[j],
                icovar));
        }
        if (minDist > clusterSize)
            initialCentres.push_back(_descriptors.row(i));
    }

    std::vector<std::list<cv::Mat> > clusters;
    clusters.resize(initialCentres.size());
    for (int i = 0; i < _descriptors.rows; i++) {
        int index = 0; double dist = 0, minDist = DBL_MAX;
        for (size_t j = 0; j < initialCentres.size(); j++) {
            dist = cv::Mahalanobis(_descriptors.row(i),initialCentres[j],icovar);
            if (dist < minDist) {
                minDist = dist;
                index = (int)j;
            }
        }
        clusters[index].push_back(_descriptors.row(i));
    }

    // TODO: throw away small clusters.

    Mat vocabulary;
    Mat centre = Mat::zeros(1,_descriptors.cols,_descriptors.type());
    for (size_t i = 0; i < clusters.size(); i++) {
        centre.setTo(0);
        for (std::list<cv::Mat>::iterator Ci = clusters[i].begin(); Ci != clusters[i].end(); Ci++) {
            centre += *Ci;
        }
        centre /= (double)clusters[i].size();
        vocabulary.push_back(centre);
    }

    return vocabulary;
}

}

}