Hand-Drawn Shape Recognition Using the SVM'ed Kernel

We describe an application of the novel Support Vector Machined Kernel (SVM'ed Kernel) to the Recognition of hand-drawn shapes. The SVM'ed kernel function is itself a. support vector machine classifier that is learned statistically from data using an automatically generated training set. We show that the new kernel manages to change the classical methodology of defining a feature vector for each pattern. One will only need to define features representing the similarity between two patterns allowing many details to be captured in a concise way. In addition, we illustrate that features describing a. single pattern could also be used in this new framework. In this paper we show how the SVM'ed Kernel is defined and trained for the multiclass shape recognition problem. Simulation results show that the SVM'ed Kernel outperforms all other classical kernels and is more robust to hard test sets.