The novel index of the similarity between hand-drawn sketches for machine learning
Ryosuke Fujii, Naoki Mori, Makoto Okada · 2019
We have dealt with sketches hand-drawn by human hands. They are creations including human emotions and sensibility. To handle sketches on a computer, it is necessary to have an index that quantitatively evaluates the stroke-order. We have already proposed a sketch similarity index for the pen movement by cosine similarity. However, we also found that human cognition strongly depends on the shape of a sketch when they understand what is drawn. For this reason, we propose a new similarity index, which is the old index added to a shape similarity index. As a result, the similarity index composed of cosine similarity and Structural Similarity (SSIM) has a strong positive correlation with the human evaluation of similarity. Then, the correlation coefficient is 0.7566. Therefore, we define an objective evaluation model for the stroke-order similarity that incorporates not only the pen movement of a sketch but also the shape of one, and we verify its effectiveness by our experiments.