Indonesian fruits classification from image using MPEG‐7 descriptors and ensemble of simple classifiers,Journal of Food Process Engineering

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Indonesian fruits classification from image using MPEG‐7 descriptors and ensemble of simple classifiers
Journal of Food Process Engineering ( IF 2.7 ) Pub Date : 2020-03-26 , DOI: 10.1111/jfpe.13414
Joko Siswantoro ₁ , Heru Arwoko ₁ , Monica Widiasri ₁

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Fruits classification from image is a very challenging task, particularly for Indonesian indigenous fruits, due to some similarities occurred in several types of the fruits. This study proposes a method to classify Indonesian fruits from image using MPEG‐7 color and texture descriptors. The descriptors were directly extracted from the image without pre‐processing and segmentation steps. Principle component analysis was then applied to reduce the dimension of the descriptors. Four simple classifiers, decision tree, naïve Bayesian, linear discriminant analysis, and k‐nearest neighbor were used to classify the fruit image based on extracted descriptors. An ensemble of simple classifiers trained with some combination of MPEG‐7 descriptors has been constructed to increase the classification accuracy of single simple classifier. To validate the proposed method, an Indonesian fruit images data set consisted of 15 classes was developed in this study. The experiment result showed that the ensemble of simple classifiers achieved the best accuracy of 97.80% by employing linear discriminant analysis, and k‐nearest neighbor as base classifiers trained using CSD, SCD, and the combination of CLD and EHD. Therefore, the proposed method achieved a good classification accuracy and can be applied in vision‐based classification system in industry.

中文翻译：

使用MPEG-7描述符和简单分类器的集成从图像对印尼水果进行分类

根据图像对水果进行分类是一项非常具有挑战性的任务，特别是对于印尼本土水果而言，由于几种类型的水果存在某些相似之处。这项研究提出了一种使用MPEG-7颜色和纹理描述符从图像中对印尼水果进行分类的方法。描述符是直接从图像中提取的，无需预处理和分割步骤。然后应用主成分分析来减少描述符的维数。四个简单的分类器，决策树，朴素贝叶斯，线性判别分析和k最近邻被用于基于提取的描述符对水果图像进行分类。构造了一组简单的分类器，它们经过MPEG-7描述符的某种组合训练，以提高单个简单分类器的分类精度。为了验证所提出的方法，本研究开发了由15个类别组成的印尼水果图像数据集。实验结果表明，简单分类器的集合通过使用线性判别分析和k最近邻作为使用CSD，SCD以及CLD和EHD组合训练的基本分类器，达到了97.80％的最佳准确性。因此，该方法具有良好的分类精度，可应用于工业中基于视觉的分类系统。以及CLD和EHD的结合。因此，该方法具有良好的分类精度，可应用于工业中基于视觉的分类系统。以及CLD和EHD的结合。因此，该方法具有良好的分类精度，可应用于工业中基于视觉的分类系统。

更新日期：2020-03-26

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