Combining geometric matching with SVM to improve symbol spotting
Nibal Nayef, Thomas Michael Breuel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
Symbol spotting is important for automatic interpretation of technical line drawings. Current spotting methods are not reliable enough for such tasks due to low precision rates. In this paper, we combine a geometric matching-based spotting method with an SVM classifier to improve the precision of the spotting. In symbol spotting, a query symbol is to be located within a line drawing. Candidate matches can be found, however, the found matches may be true or false. To distinguish a false match, an SVM classifier is used. The classifier is trained on true and false matches of a query symbol. The matches are represented as vectors that indicate the qualities of how well the query features are matched, those qualities are obtained via geometric matching. Using the classification, the precision of the spotting improved from an average of 76.6% to an average of 97.2% on a database of technical line drawings.