A Proposed Method to Acquire More Geometric Features from Hand-Drawn Sketches.

We introduce a novel method for learning geometric features of hand-drawn sketches. The method is based on two models: the first depends on a newly-designed algorithm based on the Decision Tree (DT) technique; while the other is based on the Feed-forward Neural Network (FNN) technique. Each model consists of training and testing phase, we extract features of object and build the knowledge base in training phase. In testing phase, we test the learning ability to appear advantages of using DT with new objects and FNN with large data. The results of our method show desirable performance in learning. Experiments on TU-Berlin, QuickDraw, and our own dataset reveal the effectiveness of the method. We achieve learning accuracy 99.98% on our own dataset, 98.07% on TU-Berlin, and 96.69% on QuickDraw. Experiments signify that geometric feature representation and manipulation by our method brings about a substantial improvement over state-of-the-art methods on sketch classification. [ABSTRACT FROM AUTHOR]