Learning feature weights of symbols, with application to symbol spotting

Finding discriminant features is useful for pattern recognition applications. In this work, geometric matching is combined with linear discriminant analysis (LDA) to learn the importance of the features of symbols, and assign weights to these features accordingly. The features are the line segments of the symbols. We use geometric matching within a symbol spotting system to get information on the matching between the line segments of a query symbol and the line segments of the spotted symbols found by the spotting system (both true and false matches). The matching information is used to compute feature vectors for a query symbol. The vectors represent how well the segments of a query are matched to the segments of the true and false matches. Then, LDA is trained on these vectors to get the weights of the line segments of different query symbols. This feature weighting approach is applied in symbol spotting. Using the query weighted features, the spotting system's precision improves from an average of 71% to an average of 98%, with a speed up factor of 2.1.