Reading #13. Ink Features for Diagram Recognition (Plimmer)

Comments

Danielle

Summary

This paper addresses the issue of selecting the right set of features for sketch recognition. Since Rubine the feature based sketch recognizers have become very popular, yet the set of features that is used is somewhat empiric in each case. This paper proposes a more formal method to select the most relevant features that will lead to accurate and fast recognition for certain domain. In this case the feature selection is applied to the problem of differentiating shape versus text. For each of the sample shapes 46 features are extracted and a statistical partitioning technique is employed to find the most relevant ones. The aim is to find the optimal position of a split for each feature such that there are a minimal number of misclassified strokes. After doing this for all features the most selective and important features can be used to build a binary classification or decision tree as shown below. The results were compared with other two classifiers achieving overall better classification than these two.

Discussion

Perhaps the mayor contribution of this paper is the fact that it formally analyzes a way of selecting features and it includes a complete feature set including some new features and some of the most representative found in the literature. The decision tree structure may likely guide to misleading strokes in certain cases which makes it difficult to think that it will ever achieve perfect accuracy without the aid of geometric interpretation or other means of recognition. However this method can be very fast and for most practical purposes it provides a reliable classification.