Feature extraction method based on cascade noise elimination for sketch recognition

Freehand sketching is a very efficient means for us to communicate each other. As table PC is widely popularized, the research about sketch recognition became one of important research issue. To recognize sketch, the feature point should be extracted and then each feature point is analyzed as line or curve. However, most of feature extraction algorithms suffers from noise which is occurred from the bad drawing sketch. In this paper, we propose the feature extraction algorithm robust to noise. The proposed algorithm consists of three cascade steps: candidate feature point extraction, noise reduction, and hook elimination. At the candidate feature point extraction step, the feature points is selected among input points. Then, in second step, we reduce the noise which is occurred from the previous step by using noise reduction rule based on inner product between two neighbor vectors. Finally, the hook, which can not be eliminated from two previous steps, is eliminated by the proposed hook elimination method. The experimental result shows that the average approximation error is less than 1 about 1004 line-curve hybrid shapes, and the proposed algorithm is the good feature methods.