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 pre-
processing 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.
Keywords: Jaccard Distance, Mahalanobis distance, distance
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ABOUT THE AUTHOR
Siti Salwa Salleh
FACULTY OF COMPUTER ANDMATHEMATICAL SCIENCES, UNIVERSITY TECHNOLOGY MARA, 40450 SHAH ALAM, SELANGOR
Siti Salwa Salleh
FACULTY OF COMPUTER ANDMATHEMATICAL SCIENCES, UNIVERSITY TECHNOLOGY MARA, 40450 SHAH ALAM, SELANGOR