Tooth Color Detection Using PCA and KNN Classifier Algorithm Based on Color Moment
Journal Title: EMITTER International Journal of Engineering Technology - Year 2017, Vol 5, Issue 1
Abstract
Matching the suitable color for tooth reconstruction is an important step that can make difficulties for the dentists due to the subjective factors of color selection. Accurate color matching system is mainly result based on images analyzing and processing techniques of recognition system. This system consist of three parts, which are data collection from digital teeth color images, data preparation for taking color analysis technique and extracting the features, and data classification involve feature selection for reducing the features number of this system. The teeth images which is used in this research are 16 types of teeth that are taken from RSGM UNAIR SURABAYA. Feature extraction is taken by the characteristics of the RGB, HSV and LAB based on the color moment calculation such as mean, standard deviation, skewness, and kurtosis parameter. Due to many formed features from each color space, it is required addition method for reducing the number of features by choosing the essential information like Principal Component Analysis (PCA) method. Combining the PCA feature selection technique to the clasification process using K Nearest Neighbour (KNN) classifier algorithm can be improved the accuracy performance of this system. On the experiment result, it showed that only using KNN classifier achieve accuracy percentage up to 97.19 % in learning process using 10 fold cross validation while combining PCA with KNN classifier can reduce the 12 features to the 8 features which can improve the accuracy percentage up to 97.81 %. Adding PCA as the feature selection method can be improved the accuracy performance of this color matching system with little number of features.
Authors and Affiliations
Justiawan . , Riyanto Sigit, Zainal Arief
Keywords
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- EP ID EP313010
- DOI 10.24003/emitter.v5i1.171
- Views 116
- Downloads 0
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