diff --git a/skimage/feature/match.py b/skimage/feature/match.py
index d9f95c90..4093c6b6 100644
--- a/skimage/feature/match.py
+++ b/skimage/feature/match.py
@@ -5,8 +5,7 @@ from .match_cy import _binary_cross_check_loop
 
 
 def match_binary_descriptors(keypoints1, descriptors1, keypoints2,
-                             descriptors2, threshold=0.40, cross_check=True,
-                             return_mask=True):
+                             descriptors2, threshold=0.40, cross_check=True):
     """Match keypoints described using binary descriptors in one image to
     those in second image.
 
@@ -15,30 +14,28 @@ def match_binary_descriptors(keypoints1, descriptors1, keypoints2,
     keypoints1 : (M, 2) ndarray
         M Keypoints from the first image described using skimage.feature.brief
     descriptors1 : (M, P) ndarray
-        BRIEF descriptors of size P about M keypoints in the first image.
+        Binary descriptors of size P about M keypoints in the first image.
     keypoints2 : (N, 2) ndarray
         N Keypoints from the second image described using skimage.feature.brief
     descriptors2 : (N, P) ndarray
-        BRIEF descriptors of size P about N keypoints in the second image.
+        Binary descriptors of size P about N keypoints in the second image.
     threshold : float in range [0, 1]
         Maximum allowable hamming distance between descriptors of two keypoints
         in separate images to be regarded as a match.
     cross_check : bool
-        Cross check if True.
-    return_mask : bool
-        Return index masks mask1 and mask2 for matched keypoints from
-        keypoints1 and keypoints2 respectively.
+        If True, the matched keypoints are returned after cross checking i.e. a
+        matched pair (keypoint1, keypoint2) is returned iff keypoint2 is the best
+        match for keypoint1 in second image and keypoint1 is the best match for
+        keypoint2 in first image.
 
     Returns
     -------
-    match_keypoints_pairs : (Q, 2, 2) ndarray
+    matches : (Q, 2, 2) ndarray
         Location of Q matched keypoint pairs from two images.
     mask1 : (Q,) ndarray
-        Indices of keypoints in keypoints1 that have been matched. Returned
-        only when return_mask is True.
+        Indices of keypoints in keypoints1 that have been matched.
     mask2 : (Q,) ndarray
-        Indices of keypoints in keypoints2 that have been matched. Returned
-        only when return_mask is True.
+        Indices of keypoints in keypoints2 that have been matched.
 
     """
     if (keypoints1.shape[0] != descriptors1.shape[0]
@@ -61,28 +58,22 @@ def match_binary_descriptors(keypoints1, descriptors1, keypoints2,
                                                  matched_keypoints2_index,
                                                  distance, threshold)
 
-        matched_keypoint_pairs = np.zeros((matched_index.shape[0], 2, 2),
+        matches = np.zeros((matched_index.shape[0], 2, 2),
                                           dtype=np.intp)
         mask1 = matched_index[:, 0]
         mask2 = matched_index[:, 1]
-        matched_keypoint_pairs[:, 0, :] = keypoints1[mask1]
-        matched_keypoint_pairs[:, 1, :] = keypoints2[mask2]
-        if return_mask:
-            return (matched_keypoint_pairs, mask1, mask2)
-        else:
-            return matched_keypoint_pairs
+        matches[:, 0, :] = keypoints1[mask1]
+        matches[:, 1, :] = keypoints2[mask2]
 
     else:
         temp = distance > threshold
         row_check = np.any(~temp, axis=1)
         matched_keypoints2 = keypoints2[np.argmin(distance, axis=1)]
-        matched_keypoint_pairs = np.zeros((np.sum(row_check), 2, 2),
+        matches = np.zeros((np.sum(row_check), 2, 2),
                                           dtype=np.intp)
-        matched_keypoint_pairs[:, 0, :] = keypoints1[row_check]
-        matched_keypoint_pairs[:, 1, :] = matched_keypoints2[row_check]
+        matches[:, 0, :] = keypoints1[row_check]
+        matches[:, 1, :] = matched_keypoints2[row_check]
         mask1 = np.where(row_check == True)
         mask2 = np.argmin(distance, axis=1)[row_check]
-        if return_mask:
-            return (matched_keypoint_pairs, mask1, mask2)
-        else:
-            return matched_keypoint_pairs
+
+    return matches, mask1, mask2
diff --git a/skimage/feature/match_cy.pyx b/skimage/feature/match_cy.pyx
index 8a1bb461..6574d573 100644
--- a/skimage/feature/match_cy.pyx
+++ b/skimage/feature/match_cy.pyx
@@ -5,15 +5,12 @@ def _binary_cross_check_loop(Py_ssize_t[:] matched_keypoints1_index,
                              Py_ssize_t[:] matched_keypoints2_index,
                              double[:, ::1] distance, double threshold):
     cdef Py_ssize_t i
-    #matched_index = []
-
     cdef Py_ssize_t count = 0
     cdef Py_ssize_t[:, ::1] matched_index = np.zeros((len(matched_keypoints1_index), 2), dtype=np.intp)
 
     for i in range(len(matched_keypoints1_index)):
         if (matched_keypoints2_index[matched_keypoints1_index[i]] == i and
             distance[i, matched_keypoints1_index[i]] < threshold):
-            #matched_index.append([i, matched_keypoints1_index[i]])
             matched_index[count, 0] = i
             matched_index[count, 1] = matched_keypoints1_index[i]
             count += 1
diff --git a/skimage/feature/orb_cy.pyx b/skimage/feature/orb_cy.pyx
index e312f95b..e7197797 100644
--- a/skimage/feature/orb_cy.pyx
+++ b/skimage/feature/orb_cy.pyx
@@ -9,7 +9,7 @@ import numpy as np
 from libc.math cimport sin, cos, M_PI, round
 
 
-pos = np.loadtxt("skimage/feature/orb_descriptor_positions.txt", dtype=np.int8)
+pos = np.loadtxt("orb_descriptor_positions.txt", dtype=np.int8)
 pos0 = np.ascontiguousarray(pos[:, :2])
 pos1 = np.ascontiguousarray(pos[:, 2:])