Indonesian fruits classification from image using MPEG‐7 descriptors and ensemble of simple classifiers

Siswantoro, Joko and Arwoko, Heru and Widiasri, Monica (2020) Indonesian fruits classification from image using MPEG‐7 descriptors and ensemble of simple classifiers. Journal of Food Process Engineering, 43 (4). pp. 1-13. ISSN 1745-4530

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Abstract

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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Joko Siswantoro
Date Deposited:	03 Apr 2020 04:11
Last Modified:	05 Jan 2023 03:19
URI:	http://repository.ubaya.ac.id/id/eprint/37713

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