Writer identification using textural features

Lazrak, Said; Semma, Abdelillah; Kaab, Noureddine Ahmer El; Kettani, Mohamed El Youssfi El; Mentagui, Driss

doi:10.1051/itmconf/20224301027

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The capable classes are adaptive to new data, hence the system is able to generalize or predict new data with high accuracy. There are several classifiers usable for obtaining the model class, among others KNN (Yasiran et al, 2021), harris fragments model and classification KNN method (Lazrak et al, 2022), CNN (Ma & Zhang, 2021), GAT (Series, 2020), Deep CNN (Guo et al, 2020), SVM (Naveen et al, 2020) In the present paper, the authors offer a solution for classification problems in getting a model class with the combination of Minimum Overlap Probability (MOP) (Anwar et al, 2016)and Neural Network (NN). MOP is a feature selection method to get the strongest feature and NN as the classifier.…”

Section: Introductionmentioning

confidence: 99%

The Identification of Beef and Pork Using Neural Network Based on Texture Features

Anwar¹,

Setyowibowo²

2022

JER is an international, peer-reviewed journal that publishes f

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The actual problem that frequently happens related to meat sales at conventional markets is the manipulation of pork and beef. It can happen as both visual textures bear resemblances. Texture is a crucial part of an object. In image processing, textures can be used for classification, recognition or prediction of an image. This paper offers the Minimum Overlap Probability - Neural Network method for the identification of digital image features of pork and beef.. Minimum Overlap Probability was employed to select features of the strongest characteristics, whilst Neural Network is used for training and classification. Based on the test results, the strongest features are maximum probability, contrast, sum average, autocorrelation, and energy and entropy sum. Based on MOP-NN Model test result, the digital image identification of beef and pork has performance with an accuracy of 96% on 400 images of sample data.

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