International Journal of Emerging Trends in Engineering Research · 2020 · 19 citations · 8 references
ComputingComputer ScienceFood ScienceComputer VisionFeature EngineeringMachine LearningClassificationColor AnalysisImage ClassificationPattern RecognitionSupport Vector MachinesTexture AnalysisColor ClassificationColor DescriptorsPattern Recognition ModelsSupport Vector ClassificationSupport Vector Machine ClassificationTexture ClassificationTexture DiscriminationTexture Features
This paper is about separating the two classes of civet coffee with the non-civet coffee. Image processing was used to extract the color and texture features of the civet and non-civet coffee, namely the red, green, blue, hue , saturation, brightness, entropy, energy, contrast, homogeneity. The 23 classifiers of MATLAB Classification Learner APP were used to classify the ten features. Among the 23 classifiers, the best in terms of accuracy is the quadratic support vector machine which achieved 79.7 % accuracy.
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