Assessing the Use of Similarity Distance Measurement in Shape Recognition
نویسندگان
چکیده
Distance measure is one of the techniques widely used to measure the similarity between two feature matrices of objects. The objective of this paper is to explore researches on applied distance measures in shape-based recognition. In distance measures computation, patterns that are similar will have a small distance while uncorrelated pattern in the feature space will have a far a part distance. The search for effective distance measures of shape recognition is always active as each measure suffers certain drawbacks and it must be selected appropriately to handle chosen shape features of the objects. Thus in this paper, the Chord, Cosine, Euclidean, Mahalanobis, Trigonometric and Jaccard distance were reviewed and discussed in terms of their contributions, measures strengths and weaknesses. It was found that Jaccard and Mahalanobis have their strengths that they were selected to guide in justifying and identifying appropriate distance measures of our future work on two dimensional sketching images. The new distance measure is expected to perform better and capable to obtain significance degree of accuracy and recognition rate for real time recognition for automatic classifier.
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