diff --git a/doc/examples/segmentation/plot_boundary_merge.py b/doc/examples/segmentation/plot_boundary_merge.py
new file mode 100644
index 00000000..5c649195
--- /dev/null
+++ b/doc/examples/segmentation/plot_boundary_merge.py
@@ -0,0 +1,36 @@
+from skimage import data, io, segmentation, color
+from skimage.future import graph
+import numpy as np
+
+
+def weight_boundary(graph, src, dst, n):
+    if graph.has_edge(src, n) and graph.has_edge(dst, n):
+        count_src = graph[src][n]['count']
+        count_dst = graph[dst][n]['count']
+
+        weight_src = graph[src][n]['weight']
+        weight_dst = graph[dst][n]['weight']
+
+        count = count_src + count_dst
+        return {
+            'count': count,
+            'weight': (count_src*weight_src + count_dst*weight_dst)/count
+        }
+
+    elif graph.has_edge(src, n):
+        return graph[src][n]
+    elif graph.has_edge(dst, n):
+        return graph[dst][n]
+
+
+def merge_boundary(graph, src, dst):
+    pass
+
+img = data.coffee()
+labels = segmentation.slic(img, compactness=30, n_segments=400)
+g = graph.rag_mean_color(img, labels)
+
+labels2 = graph.merge_hierarchical(labels, g, thresh=40, rag_copy=False,
+                                   in_place_merge=True,
+                                   merge_func=merge_boundary,
+                                   weight_func=weight_boundary)
diff --git a/skimage/future/graph/rag.py b/skimage/future/graph/rag.py
index 480ef4eb..d0d26d84 100644
--- a/skimage/future/graph/rag.py
+++ b/skimage/future/graph/rag.py
@@ -74,7 +74,7 @@ def min_weight(graph, src, dst, n):
     default = {'weight': np.inf}
     w1 = graph[n].get(src, default)['weight']
     w2 = graph[n].get(dst, default)['weight']
-    return min(w1, w2)
+    return {'weight': min(w1, w2)}
 
 
 def _add_edge_filter(values, graph):
@@ -171,12 +171,12 @@ class RAG(nx.Graph):
         src, dst : int
             Nodes to be merged.
         weight_func : callable, optional
-            Function to decide edge weight of edges incident on the new node.
-            For each neighbor `n` for `src and `dst`, `weight_func` will be
-            called as follows: `weight_func(src, dst, n, *extra_arguments,
+            Function to decide the attributes of edges incident on the new
+            node. For each neighbor `n` for `src and `dst`, `weight_func` will
+            be called as follows: `weight_func(src, dst, n, *extra_arguments,
             **extra_keywords)`. `src`, `dst` and `n` are IDs of vertices in the
-            RAG object which is in turn a subclass of
-            `networkx.Graph`.
+            RAG object which is in turn a subclass of `networkx.Graph`. It is
+            expected to return a dict of attributes of the resulting edge.
         in_place : bool, optional
             If set to `True`, the merged node has the id `dst`, else merged
             node has a new id which is returned.
@@ -207,9 +207,9 @@ class RAG(nx.Graph):
             self.add_node(new)
 
         for neighbor in neighbors:
-            w = weight_func(self, src, new, neighbor, *extra_arguments,
-                            **extra_keywords)
-            self.add_edge(neighbor, new, weight=w)
+            data = weight_func(self, src, new, neighbor, *extra_arguments,
+                               **extra_keywords)
+            self.add_edge(neighbor, new, attr_dict=data)
 
         self.node[new]['labels'] = (self.node[src]['labels'] +
                                     self.node[dst]['labels'])
diff --git a/skimage/future/graph/tests/test_rag.py b/skimage/future/graph/tests/test_rag.py
index 4b2e4cff..dadbe5ce 100644
--- a/skimage/future/graph/tests/test_rag.py
+++ b/skimage/future/graph/tests/test_rag.py
@@ -10,7 +10,7 @@ def max_edge(g, src, dst, n):
     default = {'weight': -np.inf}
     w1 = g[n].get(src, default)['weight']
     w2 = g[n].get(dst, default)['weight']
-    return max(w1, w2)
+    return {'weight': max(w1, w2)}
 
 
 @skipif(not is_installed('networkx'))
@@ -113,7 +113,7 @@ def test_rag_error():
 def _weight_mean_color(graph, src, dst, n):
     diff = graph.node[dst]['mean color'] - graph.node[n]['mean color']
     diff = np.linalg.norm(diff)
-    return diff
+    return {'weight': diff}
 
 
 def _pre_merge_mean_color(graph, src, dst):