Recognizing objects using color-annotated adjacency graphs

We introduce a new algorithm for identifying objects in cluttered images, based on approximate subgraph matching. This algorithm is robust under moderate variations in the camera viewpoints. In other words, it is expected to recognize an object (whose model is derived from a template image) in a search image, even when the cameras of the template and search images are substantially different. The algorithm represents the objects in the template and search images by weighted adjacency graphs. Then the problem of recognizing the template object in the search image is reduced to the problem of approximately matching the template graph as a subgraph of the search image graph. The matching procedure is somewhat insensitive to minor graph variations, thus leading to a recognition algorithm which is robust with respect to camera variations.