Matching images based on consistency graph and region adjacency graphs

The image matching methods based on regions have many advantages over the point matching techniques, and the most charming one is that once region being matched, all pixels are matched in theory. It would benefit many applications, such as object retrieval, stereo corresponding, semantic understanding a scene, object tracking. This paper proposes a new region matching algorithm based on consistency graph and region adjacency graphs. Firstly, the segmented images are transformed into region adjacency graphs, and the potential region pairs and the potential edge segment pairs are packaged in a consistency graph. Since the rightly matched pair always is accompanied by harmonious neighbourhoods, the right correspondences tend to cluster together, and the error corresponding relationship should have few chances to connect to any compatible neighbourhood. Thus, the solution space is greatly reduced and the corresponding relationship can be found in a polynomial computational complexity just by a simple method, such as seed-growth method. To the best of our knowledge, the method is the first one to match two images by region adjacency graphs and find the corresponding relationship in a polynomial computational complexity. Experiments on the existing benchmark show that the proposed method could quickly find the right corresponding relationship between images with illumination, rotation and affine transformation.