ИСТИНА

In this paper we propose and test a novel approach, namely Supervised Asymmetric Metric Extraction (SAME), that learns from the supervised metric data and extracts the best single metric from a given set of metrics. It takes up large space to represent the metric-based descriptions, so the approach is specifically crafted to allow for a computationally effective solution. The proposed learning model is scale - independent and hence rescaling of any metric does not affect the learning. Another advantage of metric extraction is the way of training set annotation which specifically suits verification problems. In this metric extraction approach, we separate intraclass and interclass distances, simplifying the metric extraction problem to linear programming problem which can use optimization techniques effectively. Here, the number of variables needed in the computation remains small and hence it eliminates the need of any soft constraint extension resulting in reduced computational time complexity. The experimental results on offline and online signature data demonstrate that the proposed approach yields better performance and time complexity compared to other metric extraction technique as computational complexity in the proposed approach depends mainly on the calculation of original distances.