Combining Mahalanobis and Jaccard Distance to Overcome Similarity Measurement Constriction on Geometrical Shapes

In this study Jaccard Distance was performed by measuring the asymmetric information on binary variable and the comparison between vectors component. It compared two objects and notified the degree of similarity of these objects. After thorough preprocessing tasks; like translation, rotation, invariance scale content and noise resistance done onto the hand sketch object, Jaccard distance still did not show significance improvement. Hence this paper combined Mahalanobis measure with Jaccard distance to improve the similarity performances. It started with the same pre-processing tasks and feature analysis, shape normalization, shape perfection and followed with binary data conversion. Then each edge of the geometric shape was separated and measured using Jaccard distance. The shapes that passed the threshold value were measured by Mahalanobis distance. The results showed that the similarity percentage had increased from 61% to 84%, thus accrued an improved average of 21.6% difference.