Abstract
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 a previous work, 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.
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