Abstract
At present, many popular methods for object recognition are based on regional visual feature vectors which ignore the global structure of the object, leading to the “semantic gap” between image content and its tag. Graph-based structural representation of an object can record regional visual features and global relationships between regions that compose the object. However, the computation complexity of graph comparing limits the application of graph model in object recognition. One method for improving the recognizing speed is to reduce the prototypes in concept space of specific object category. We presented a prototype selection method for sythetic object recognition based on structural graph model using greedy clustering and representative sample selection. Experiments are conducted on a 2-D CAD synthetic object database. And the results show that the prototype selection method reduces the time cost of object recognition greatly with an acceptable loss in accuracy.
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