Name
Title | ||
|---|---|---|
Combining Mahalanobis and Jaccard to Improve Shape Similarity Measurement in Sketch Recognition |
Abstract | ||
|---|---|---|
Mahalanobis, Jaccard and others are similarity measurements which are commonly used in sketch recognition. Attempts to improve similarity measurement can be made by manipulating formulae and reducing the testing data set used but less effort are attempted to propose algorithm. Hence, the purpose of this study is to propose a new algorithm for a better method in shape recognition. To do so, Mahalanobis and Jaccard distance measures were combined to improve the similarity measure. The pre-processing involved feature analysis, shape normalization and shape perfection and data conversion into a binary. In the new algorithm, each edge of the geometric shape was separated and measured using Jaccard distance. 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. Having this difference, the three outcomes of this study were a combined algorithm, a new technique of separating the strokes in Jaccard, and lastly, the use of extreme vertices in Mahalanobis similarity measurement to reduce computation time. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/UKSIM.2011.67 | UKSim |
Keywords | Field | DocType |
combined algorithm,shape recognition,testing data set,mahalanobis distance,jaccard distance measure,shape perfection,feature analysis,sketch recognition,combining mahalanobis,similarity percentage,geometric shape,jaccard distance,improve shape similarity measurement,mahalanobis similarity measurement,edge detection,shape measurement,shape similarity measurement,similarity measurement,masking technique,similarity measure,new algorithm,shape normalization,data conversion,algorithm design and analysis,pixel,mathematical model,algorithm design,diamond like carbon,shape | Normalization (statistics),Pattern recognition,Similarity measure,Overlap coefficient,Mahalanobis distance,Sketch recognition,Geometric shape,Artificial intelligence,Jaccard index,Pattern recognition (psychology),Mathematics | Conference |
ISBN | Citations | PageRank |
978-0-7695-4376-5 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siti Salwa Salleh | 1 | 0 | 1.01 |
| Noor Aznimah Abdul Aziz | 2 | 0 | 0.34 |
| Daud Mohamad | 3 | 1 | 2.40 |
| Megawati Omar | 4 | 0 | 0.34 |