Sparse Representations for Efficient Shape Matching

Graph matching is a fundamental problem with many applications in computer vision. Patterns are represented by graphs and pattern recognition corresponds to finding a correspondence between vertices from different graphs. In many cases, the problem can be formulated as a quadratic assignment problem, where the cost function consists of two components: a linear term representing the vertex compatibility and a quadratic term encoding the edge compatibility. The quadratic assignment problem is NP-hard and the present paper extends the approximation technique based on graph matching and efficient belief propagation, described in, by using sparse representations for efficient shape matching. Successful results of recognition of 3D objects and handwritten digits are illustrated, using COIL and MNIST datasets, respectively.