diff --git a/modules/xobjdetect/src/icfdetector.cpp b/modules/xobjdetect/src/icfdetector.cpp
index d2f28c143..0154d5505 100644
--- a/modules/xobjdetect/src/icfdetector.cpp
+++ b/modules/xobjdetect/src/icfdetector.cpp
@@ -71,7 +71,7 @@ void ICFDetector::train(const String& pos_path,
     glob(pos_path + "/*.png", pos_filenames);
 
     vector<String> bg_filenames;
-    glob(bg_path + "/*.png", bg_filenames);
+    glob(bg_path + "/*.jpg", bg_filenames);
 
     model_n_rows_ = params.model_n_rows;
     model_n_cols_ = params.model_n_cols;
diff --git a/modules/xobjdetect/src/stump.cpp b/modules/xobjdetect/src/stump.cpp
index d575d4c61..c9c9911d8 100644
--- a/modules/xobjdetect/src/stump.cpp
+++ b/modules/xobjdetect/src/stump.cpp
@@ -69,128 +69,115 @@ int Stump::train(const Mat& data, const Mat& labels, const Mat& weights)
     /* Assert that data and labels have int type */
     /* Assert that weights have float type */
 
+    Mat_<int> d = Mat_<int>::zeros(1, data.cols);
+    const Mat_<int>& l = labels;
+    const Mat_<float>& w = weights;
 
-    /* Prepare labels for each feature rearranged according to sorted order */
-    Mat sorted_labels(data.rows, data.cols, labels.type());
-    Mat sorted_weights(data.rows, data.cols, weights.type());
-    Mat indices;
-    sortIdx(data, indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
-    for( int row = 0; row < indices.rows; ++row )
-    {
-        for( int col = 0; col < indices.cols; ++col )
-        {
-            sorted_labels.at<int>(row, col) =
-                labels.at<int>(0, indices.at<int>(row, col));
-            sorted_weights.at<float>(row, col) =
-                weights.at<float>(0, indices.at<int>(row, col));
-        }
-    }
+    Mat_<int> indices(1, l.cols);
 
-    /* Sort feature values */
-    Mat sorted_data(data.rows, data.cols, data.type());
-    sort(data, sorted_data, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
+    Mat_<int> sorted_d(1, data.cols);
+    Mat_<int> sorted_l(1, l.cols);
+    Mat_<float> sorted_w(1, w.cols);
 
-    /* Split positive and negative weights */
-    Mat pos_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
-    Mat neg_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
+
+    Mat_<float> pos_c_w = Mat_<float>::zeros(1, w.cols);
+    Mat_<float> neg_c_w = Mat_<float>::zeros(1, w.cols);
+
+
+    float min_err = FLT_MAX;
+    int min_row = -1;
+    int min_thr = -1;
+    int min_pol = -1;
+    float min_pos = 1;
+    float min_neg = -1;
+    float eps = 1.0f / (4 * l.cols);
+
+    /* For every feature */
     for( int row = 0; row < data.rows; ++row )
     {
         for( int col = 0; col < data.cols; ++col )
-        {
-            if( sorted_labels.at<int>(row, col) == +1 )
-            {
-                pos_weights.at<float>(row, col) =
-                    sorted_weights.at<float>(row, col);
-            }
-            else
-            {
-                neg_weights.at<float>(row, col) =
-                    sorted_weights.at<float>(row, col);
-            }
-        }
-    }
+            d(0, col) = data.at<int>(row, col);
 
-    /* Compute cumulative sums for fast stump error computation */
-    Mat pos_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
-    Mat neg_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
-    cumsum(pos_weights, pos_cum_weights);
-    cumsum(neg_weights, neg_cum_weights);
+        sortIdx(d, indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
 
-    /* Compute total weights of positive and negative samples */
-    float pos_total_weight = pos_cum_weights.at<float>(0, weights.cols - 1);
-    float neg_total_weight = neg_cum_weights.at<float>(0, weights.cols - 1);
+        for( int col = 0; col < indices.cols; ++col )
+        {
+            int ind = indices(0, col);
+            sorted_d(0, col) = d(0, ind);
+            sorted_l(0, col) = l(0, ind);
+            sorted_w(0, col) = w(0, ind);
+        }
 
+        Mat_<float> pos_w = Mat_<float>::zeros(1, w.cols);
+        Mat_<float> neg_w = Mat_<float>::zeros(1, w.cols);
+        for( int col = 0; col < d.cols; ++col )
+        {
+            float weight = sorted_w(0, col);
+            if( sorted_l(0, col) == +1)
+                pos_w(0, col) = weight;
+            else
+                neg_w(0, col) = weight;
+        }
 
-    float eps = 1.0f / (4 * labels.cols);
+        cumsum(pos_w, pos_c_w);
+        cumsum(neg_w, neg_c_w);
 
-    /* Compute minimal error */
-    float min_err = FLT_MAX;
-    int min_row = -1;
-    int min_col = -1;
-    int min_polarity = 0;
-    float min_pos_value = 1, min_neg_value = -1;
+        float pos_total_w = pos_c_w(0, w.cols - 1);
+        float neg_total_w = neg_c_w(0, w.cols - 1);
 
-    for( int row = 0; row < sorted_weights.rows; ++row )
-    {
-        for( int col = 0; col < sorted_weights.cols - 1; ++col )
+        for( int col = 0; col < w.cols - 1; ++col )
         {
             float err, h_pos, h_neg;
+            float pos_wrong, pos_right;
+            float neg_wrong, neg_right;
 
-            // Direct polarity
+            /* Direct polarity */
 
-            float pos_wrong = pos_cum_weights.at<float>(row, col);
-            float pos_right = pos_total_weight - pos_wrong;
+            pos_wrong = pos_c_w(0, col);
+            pos_right = pos_total_w - pos_wrong;
 
-            float neg_right = neg_cum_weights.at<float>(row, col);
-            float neg_wrong = neg_total_weight - neg_right;
-
-            h_pos = (float)(.5 * log((pos_right + eps) / (pos_wrong + eps)));
-            h_neg = (float)(.5 * log((neg_wrong + eps) / (neg_right + eps)));
+            neg_right = neg_c_w(0, col);
+            neg_wrong = neg_total_w - neg_right;
 
             err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right);
 
+            h_pos = .5f * log((pos_right + eps) / (pos_wrong + eps));
+            h_neg = .5f * log((neg_wrong + eps) / (neg_right + eps));
+
             if( err < min_err )
             {
                 min_err = err;
                 min_row = row;
-                min_col = col;
-                min_polarity = +1;
-                min_pos_value = h_pos;
-                min_neg_value = h_neg;
+                min_thr = (sorted_d(0, col) + sorted_d(0, col + 1)) / 2;
+                min_pol = +1;
+                min_pos = h_pos;
+                min_neg = h_neg;
             }
 
-            // Opposite polarity
+            /* Opposite polarity */
+
             swap(pos_right, pos_wrong);
             swap(neg_right, neg_wrong);
 
-            h_pos = -h_pos;
-            h_neg = -h_neg;
-
             err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right);
 
-
             if( err < min_err )
             {
                 min_err = err;
                 min_row = row;
-                min_col = col;
-                min_polarity = -1;
-                min_pos_value = h_pos;
-                min_neg_value = h_neg;
+                min_thr = (sorted_d(0, col) + sorted_d(0, col + 1)) / 2;
+                min_pol = -1;
+                min_pos = -h_pos;
+                min_neg = -h_neg;
             }
+
         }
     }
 
-    /* Compute threshold, store found values in fields */
-    threshold_ = ( sorted_data.at<int>(min_row, min_col) +
-                   sorted_data.at<int>(min_row, min_col + 1) ) / 2;
-    polarity_ = min_polarity;
-    pos_value_ = min_pos_value;
-    neg_value_ = min_neg_value;
+    threshold_ = min_thr;
+    polarity_ = min_pol;
+    pos_value_ = min_pos;
+    neg_value_ = min_neg;
 
     return min_row;
 }