diff --git a/openbr/plugins/tree.cpp b/openbr/plugins/tree.cpp
new file mode 100644
index 0000000..b75fa0f
--- /dev/null
+++ b/openbr/plugins/tree.cpp
@@ -0,0 +1,149 @@
+#include <opencv2/ml/ml.hpp>
+
+#include "openbr_internal.h"
+#include "openbr/core/opencvutils.h"
+#include <QString>
+#include <QTemporaryFile>
+
+using namespace std;
+using namespace cv;
+
+namespace br
+{
+
+static void storeForest(const CvRTrees &forest, QDataStream &stream)
+{
+    // Create local file
+    QTemporaryFile tempFile;
+    tempFile.open();
+    tempFile.close();
+
+    // Save MLP to local file
+    forest.save(qPrintable(tempFile.fileName()));
+
+    // Copy local file contents to stream
+    tempFile.open();
+    QByteArray data = tempFile.readAll();
+    tempFile.close();
+    stream << data;
+}
+
+static void loadForest(CvRTrees &forest, QDataStream &stream)
+{
+    // Copy local file contents from stream
+    QByteArray data;
+    stream >> data;
+
+    // Create local file
+    QTemporaryFile tempFile(QDir::tempPath()+"/forest");
+    tempFile.open();
+    tempFile.write(data);
+    tempFile.close();
+
+    // Load MLP from local file
+    forest.load(qPrintable(tempFile.fileName()));
+}
+
+/*!
+ * \ingroup transforms
+ * \brief Wraps OpenCV's random trees framework
+ * \author Scott Klum \cite sklum
+ * \brief http://docs.opencv.org/modules/ml/doc/random_trees.html
+ */
+class ForestTransform : public MetaTransform
+{
+    Q_OBJECT
+    Q_PROPERTY(bool classification READ get_classification WRITE set_classification RESET reset_classification STORED true)
+    Q_PROPERTY(float splitPercentage READ get_splitPercentage WRITE set_splitPercentage RESET reset_splitPercentage STORED true)
+    Q_PROPERTY(int maxDepth READ get_maxDepth WRITE set_maxDepth RESET reset_maxDepth STORED true)
+    Q_PROPERTY(int maxTrees READ get_maxTrees WRITE set_maxTrees RESET reset_maxTrees STORED true)
+    Q_PROPERTY(float forestAccuracy READ get_forestAccuracy WRITE set_forestAccuracy RESET reset_forestAccuracy STORED true)
+    Q_PROPERTY(bool returnConfidence READ get_returnConfidence WRITE set_returnConfidence RESET reset_returnConfidence STORED true)
+    Q_PROPERTY(bool overwriteMat READ get_overwriteMat WRITE set_overwriteMat RESET reset_overwriteMat STORED true)
+    Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
+    Q_PROPERTY(QString outputVariable READ get_outputVariable WRITE set_outputVariable RESET reset_outputVariable STORED false)
+    BR_PROPERTY(bool, classification, true)
+    BR_PROPERTY(float, splitPercentage, .01)
+    BR_PROPERTY(int, maxDepth, std::numeric_limits<int>::max())
+    BR_PROPERTY(int, maxTrees, 10)
+    BR_PROPERTY(float, forestAccuracy, .1)
+    BR_PROPERTY(bool, returnConfidence, true)
+    BR_PROPERTY(bool, overwriteMat, true)
+    BR_PROPERTY(QString, inputVariable, "Label")
+    BR_PROPERTY(QString, outputVariable, "")
+
+    CvRTrees forest;
+
+    void train(const TemplateList &data)
+    {
+        Mat samples = OpenCVUtils::toMat(data.data());
+        Mat labels = OpenCVUtils::toMat(File::get<float>(data, inputVariable));
+
+        Mat types = Mat(samples.cols + 1, 1, CV_8U);
+        types.setTo(Scalar(CV_VAR_NUMERICAL));
+
+        if (classification) {
+            types.at<char>(samples.cols, 0) = CV_VAR_CATEGORICAL;
+        } else {
+            types.at<char>(samples.cols, 0) = CV_VAR_NUMERICAL;
+        }
+
+        int minSamplesForSplit = data.size()*splitPercentage;
+        forest.train( samples, CV_ROW_SAMPLE, labels, Mat(), Mat(), types, Mat(),
+                    CvRTParams(maxDepth,
+                               minSamplesForSplit,
+                               0,
+                               false,
+                               2,
+                               0, // priors
+                               false,
+                               0,
+                               maxTrees,
+                               forestAccuracy,
+                               CV_TERMCRIT_EPS));
+
+        qDebug() << "Number of trees:" << forest.get_tree_count();
+    }
+
+    void project(const Template &src, Template &dst) const
+    {
+        dst = src;
+
+        float response;
+        if (classification && returnConfidence) {
+            // Fuzzy class label
+            response = forest.predict_prob(src.m().reshape(1,1));
+        } else {
+            response = forest.predict(src.m().reshape(1,1));
+        }
+
+        if (overwriteMat) {
+            dst.m() = Mat(1, 1, CV_32F);
+            dst.m().at<float>(0, 0) = response;
+        } else {
+            dst.file.set(outputVariable, response);
+        }
+    }
+
+    void load(QDataStream &stream)
+    {
+        loadForest(forest,stream);
+    }
+
+    void store(QDataStream &stream) const
+    {
+        storeForest(forest,stream);
+    }
+
+    void init()
+    {
+        if (outputVariable.isEmpty())
+            outputVariable = inputVariable;
+    }
+};
+
+BR_REGISTER(Transform, ForestTransform)
+
+} // namespace br
+
+#include "tree.moc"