NDLI: Multiclass amber gemstones classification with various segmentation and committee strategies

Content Provider	IEEE Xplore Digital Library
Author	Sinkevicius, S. Lipnickas, A. Rimkus, K.
Copyright Year	2013
Description	Author affiliation: Dept. of Control Technol., Kaunas Univ. of Technol., Kaunas, Lithuania (Sinkevicius, S.; Lipnickas, A.; Rimkus, K.)
Abstract	The amber gemstones classification system is proposed and described in this paper. The amber data used in experiments are collected by amber art craft industry experts and divided manually into 30 classes. The presented investigations were care out in order to find out most accurate and fast classifier for online amber sorting application. QDA, KNN, RBF, and decision tree classifiers were tested. The descriptive features of amber were chosen as the mean, standard deviation, kurtosis, and skewness calculated on amber pixels from grayscale and HSV color spaces. The best classification result in terms of accuracy and computational performance based on the features calculation on the all pixels of sample was 60.30 % accuracy, obtained by pruned decision tree classifier. In order to improve the classification results, the pixels of amber samples were grouped into predefined concentric ring segments and best acquired result was 71.31 %. Then the final improvement was introduced by forming a committee of decision tree classifiers with Half&Half method which increased accuracy up to 73.18 %.
Starting Page	304
Ending Page	308
File Size	218142
Page Count	5
File Format	PDF
ISBN	9781479914265
e-ISBN	9781479914296
DOI	10.1109/IDAACS.2013.6662694
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-12
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Accuracy Decision trees Image segmentation Training Optical sensors Sorting supervised learning computer vision expert systems image classification
Content Type	Text
Resource Type	Article

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