Fruits Classification from Image using MPEG-7 Visual Descriptors and Extreme Learning Machine

Siswantoro, Joko and Arwoko, Heru and Siswantoro, M. Z. F. N (2020) Fruits Classification from Image using MPEG-7 Visual Descriptors and Extreme Learning Machine. In: The 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 10 December 2020, Grand Inna Malioboro Hotel, Yogyakarta.

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Abstract

Fruit image classification has several applications and can be used as alternative to traditionally fruit classification performed by human expert. This paper aims to propose fruits classification method from image using extreme learning machine (ELM), MPEG-7 visual descriptors, and principal component analysis (PCA). The optimum parameters of ELM and PCA were determined using grid search optimization. The best classification performance of 97.33% has been achieved in classifying Indonesian fruit images consisted of 15 classes. By applying the ensemble of ELMs, the classification accuracy was increased to 98.03%. This result shows that the proposed method produces high classification performance.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Indonesia fruit; fruit image; extreme learning machine; MPEG-7 visual description; classification
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Joko Siswantoro
Date Deposited:	27 Jan 2021 05:55
Last Modified:	27 Jan 2022 04:14
URI:	http://repository.ubaya.ac.id/id/eprint/38789

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