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Alternating Direction Graph Matching (ADGM)

This is a Python implementation of Alternating Direction Graph Matching (ADGM), which was introduced in the paper Alternating Direction Graph Matching (CVPR 2017) by D. Khuê Lê-Huu and Nikos Paragios.

A C++ implementation (with MATLAB wrapper) for hyper-graphs can be found here: https://github.com/netw0rkf10w/ADGM. I will add support for hyper-graphs to this repo in the future.

If you use any part of this code, please cite:

@inproceedings{lehuu2017adgm,
  author    = {D. Khu{\^{e}} L{\^{e}}{-}Huu and Nikos Paragios},
  title     = {Alternating Direction Graph Matching},
  booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2017, Honolulu, HI, USA, July 21-26, 2017},
  pages     = {4914--4922},
  publisher = {{IEEE} Computer Society},
  year      = {2017},
  url       = {https://doi.org/10.1109/CVPR.2017.522},
  doi       = {10.1109/CVPR.2017.522}
}

Dependencies

In addition to some standard packages, it is required to intall Numba. It is strongly recommended to use a virtual environment such as conda, virtualenv, or venv.

conda install numba

To install Numba in other environments:

pip install numba

Usage

Consider two graphs G1 = (V1, E1) and G2 = (V2, E2). Denote by n1 = |V1| and n2 = |V2| the corresponding numbers of nodes. For matching G1 and G2, one needs to construct the corresponding unary and pairwise potentials (see Examples) and call ADGM as follows:

X = ADGM(U, P, assignment_mask=None, X0=None, **kwargs)

The output X is an n1 x n2 discete assignment matrix. The input arguments are described below.

  • U (numpy.ndarray): An n1 x n2 array representing the unary potentials, i.e., the costs of matching individual nodes. For example, U[i,p] can be the dissimilarity between the node i of G1 and the node p of G2. The more similar the two nodes are, the smaller the matching cost should be.
  • P (numpy.ndarray or scipy.sparse.csr_matrix): An A x A array representing the pairwiste potentials, where A is the number of non-zeros of assignment_mask (see below), i.e., the total number of match candidates. If no assignment_mask is provided, A = n1*n2. A match candidate is a pair (i, p), where i in G1 and p in G2. Such candidate can be represented by an index a (a = 0, 1,..., (A-1) that corresponds to the a-th element of the flatten assignment matrix with zero elements removed. We write a = (i,p). For two match candidates a = (i,p) and b = (j,q), the pairwise potential P[a, b] represents the dissimilarity between the vectors ij and pq. If ij is not an edge of G1 or if pq is not and edge of G2, then P[a, b] should be zero. Otherwise, P[a, b] should be non-positive.
  • assignment_mask (numpy.ndarray, optional): An n1 x n2 array representing potential match candidates: assignment_mask[i, p] = 0 means i cannot be matched to p. If you have prior information on this, you should always set this matrix as it will make matching much faster and more accurate.
  • X0 (numpy.ndarray, optional): An n1 x n2 array used for initializing ADGM.
  • kwargs (dict, optional): ADGM optimization parameters. For example:
    kwargs = {'rho': max(10**(-60.0/np.sqrt(n1*n2)), 1e-4),
              'rho_max': 100,
              'step': 1.2,
              'precision': 1e-5,
              'decrease_delta': 1e-3,
              'iter1': 5,
              'iter2': 10,
              'max_iter': 10000,
              'verbose': False}

Examples

We give some examples of using ADGM for matching two sets of synthetic feature points. To reproduce the following results, run demo.py.

Input feature points

The following code generate two set of 2D points.

import numpy as np
import random

# for reproducibility
np.random.seed(13)
random.seed(13)

# numbers of points
n1 = 40
n2 = 30
# in this example, we randomly take some points of the first set
# then randomly transform them, thus we need n1 >= n2
assert n1 >= n2

# create a set of randoms 2D points, zero-centered them
points1 = np.random.randint(100, size=(n1, 2))
points1 = points1 - np.mean(points1, axis=0)

# randomly transform it using a similarity transformation

# first construct the transformation matrix
theta = np.random.rand()*np.pi/2
# scale = np.random.uniform(low=0.5, high=1.5)
scale = 0.9
tx = np.random.randint(low=120, high=150)
ty = np.random.randint(50)
M = np.array([[scale*np.cos(theta), np.sin(theta),       tx],
                [-np.sin(theta),      scale*np.cos(theta), ty]])

# then transform the first set of points
points2 = np.ones((3, n1))
points2[:2] = np.transpose(points1)
points2 = np.transpose(np.dot(M, points2))

# randomly keep only n2 points
indices = list(range(n1))
random.shuffle(indices)
points2 = points2[indices]
points2 = points2[:n2]

# we can add random potential match candidates
# assignment_mask = np.logical_or(np.random.randn(n1, n2) > 0.5, X_gt)
# but for now let's use all the candidates
assignment_mask = None

# ground-truth matching, for evaluation
X_gt = np.zeros((n1, n2), dtype=int)
for idx2, idx1 in enumerate(indices[:n2]):
    X_gt[idx1, idx2] = 1

Let us visualize the features and the ground-truth matching.

import os
from utils import draw_results

# where to save outputs
output_dir = './output'
os.makedirs(output_dir, exist_ok=True)

# Visualize the feature points and the ground-truth matching
plot = plt.subplots()
plot[1].title.set_text('Ground-truth matching')
draw_results(plot, points1, points2, X=X_gt)
plt.show()

Ground-truth matching

Matching using fully-connected graphs

In this section, we try ADGM for matching the above set of points by defining two fully-connected graphs. To this end, we will need to define the unary and pairwise potentials.

The unary potentials represent the cost of matching individual points (e.g., a dissimilarity measure). Since in this example, the points are just plain 2D points with no attributes, we omit the unary potentials:

# We do not use any unary potentials
U = np.zeros((n1, n2))

For any pair of match candidates a = (i, p) and b = (j, q) (see Usage for notation), the pairwise potential P[a, b] can be defined, for example, as a weighted sum of d_length(ij, pq) and d_angle(ij, pq), which are respectively the differences in length and in angle between the two vectors ij and pq: P[a, b] = w * d_length(ij, pq) + (1 - w) * d_angle(ij, pq). Let d_ij, d_pq be the lengths of the vectors and alpha be the angle between them. One can, for example, define the above dissimilarity as d_length(ij, pq) = |l_ij - l_pq|/(l_ij + l_pq) - 1 and d_angle(ij, pq) = 0.5*(-cos(angle) - 1). Note that this is slightly different from what is proposed in the paper (Equations 39 and 40).

The above pairwise potentials are supported by the function build_pairwise_potentials. Once can thus build the graph matching problem and solve it using ADGM as follows:

from adgm.adgm import ADGM
from adgm.energy import build_pairwise_potentials

# Weight of the length term (the weight of the angle term is thus 1-len_weight)
len_weight = 0.7

# Build the pairwise potentials with fully-connected graphs
P_dense = build_pairwise_potentials(points1, points2, 
                assignment_mask=assignment_mask, len_weight=len_weight)

# Call ADGM solver
kwargs = {'rho': max(10**(-60.0/np.sqrt(n1*n2)), 1e-4),
          'rho_max': 100,
          'step': 1.2,
          'precision': 1e-5,
          'decrease_delta': 1e-3,
          'iter1': 5,
          'iter2': 10,
          'max_iter': 10000,
          'verbose': False}
X_dense = ADGM(U, P_dense, **kwargs)

# Plot the results
plot = plt.subplots()
plot[1].title.set_text('Fully-connected graph matching')
draw_results(plot, points1, points2, X=X_dense, X_gt=X_gt)
plt.show()

Dense matching

Green: Good matches (True positives). Red: Bad matches (False positives). Yellow: Missed matches (False negatives).

Matching using sparse graphs

Instead of using fully-connected graphs, which is computationally expensive, one can also use sparse graphs. A solution is to use approximate nearest neighbors to define graph edges. Below I give an example of using Delaunay triangulation.

The build_pairwise_potentials function has two arguments adj1 and adj2 for representing the adjacency matrices of the two point sets.

from scipy.spatial import Delaunay
from utils import get_adjacency_matrix

# Building the graphs based on Delaunay triangulation
tri1 = Delaunay(points1)
adj1 = get_adjacency_matrix(tri1)
tri2 = Delaunay(points2)
adj2 = get_adjacency_matrix(tri2)

# Build the pairwise potentials with Delaunay graphs
P_sparse = build_pairwise_potentials(points1, points2, adj1=adj1, adj2=adj2,
                assignment_mask=assignment_mask, len_weight=len_weight)

# Call ADGM solver
X_sparse = ADGM(U, P_sparse, assignment_mask=assignment_mask, **kwargs)
print('Sparse matching time (s):', time.time() - start)

# Plot the results
plot = plt.subplots()
plot[1].title.set_text('Sparse graph matching')
plt.triplot(points1[:,0], points1[:,1], tri1.simplices, color='b')
plt.triplot(points2[:,0], points2[:,1], tri2.simplices, color='b')
draw_results(plot, points1, points2, X=X_dense, X_gt=X_gt)
plt.show()

Sparse matching

Notes

  1. The very first execution of the code will be a bit slow because Numba needs to compile some functions at the first run (see here for more details).

  2. If the displacement of the sets of points are too large in terms of both scaling and rotation, it would be better to use sparse graphs instead of fully-connected ones.