Multi-image matching for object recognition

One of the central problems in object recognition is to develop appropriate representations for the objects in images. The authors present a novel approach for image representation that is based on graphs. In the proposed image graph, each node represents a patch and edges are added between neighbouring nodes. First, class-specific match-set graphs are generated by matching the image graphs that are in the same categories, and the multi-image matching problem is solved by applying a seed-expansion strategy. Then, the matches between the match-set graphs and an image graph are considered to be the object patches in the image. Finally, the features extracted from these patches are used for the image representation. Extensive experiments are conducted to demonstrate that their approach can obtain state-of-the-art results on several challenging datasets.