Combining Mahalanobis and Jaccard to Improve Shape Similarity Measurement in Sketch Recognition

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.

[1]  H. Nemmour,et al.  New Jaccard-Distance Based Support Vector Machine Kernel for Handwritten Digit Recognition , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[2]  Beiji Zou,et al.  Shape-Based Trademark Retrieval Using Cosine Distance Method , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[3]  Tracy Anne Hammond,et al.  GLADDER: Combining Gesture and Geometric Sketch Recognition , 2008, AAAI.

[4]  Jun Guo,et al.  Efficient Computation of Mahalanobis Distance in Financial Hand-Written Chinese Character Recognition , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[5]  Daud Mohamad,et al.  Investigating Jaccard Distance similarity measurement constriction on handwritten pen-based input digit , 2010, 2010 International Conference on Science and Social Research (CSSR 2010).

[6]  Jerry M. Mendel,et al.  The Linguistic Weighted Average , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[7]  Supriya Kapoor,et al.  Facial Gesture Recognition Using Correlation And Mahalanobis Distance , 2010, ArXiv.

[8]  Nualsawat Hiransakolwong,et al.  Feature Selection Using Euclidean Distance and Cosine Similarity for Intrusion Detection Model , 2009, 2009 First Asian Conference on Intelligent Information and Database Systems.