Ali, A., Mohammed, U., Nour, R. (2021). Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features. test, 2(2), 1-6. doi: 10.21608/kjis.2021.198376
Abdelmgeid Ali; Usama Sayed Mohammed; Rehab Ragaa Nour. "Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features". test, 2, 2, 2021, 1-6. doi: 10.21608/kjis.2021.198376
Ali, A., Mohammed, U., Nour, R. (2021). 'Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features', test, 2(2), pp. 1-6. doi: 10.21608/kjis.2021.198376
Ali, A., Mohammed, U., Nour, R. Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features. test, 2021; 2(2): 1-6. doi: 10.21608/kjis.2021.198376

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Kafrelsheikh Journal of Information Sciences
Article 8, Volume 2, Issue 2, September and October 2021, Pages 1-6    PDF (396.99 K)
Document Type: Original Article
DOI: 10.21608/kjis.2021.198376
Authors
Abdelmgeid Ali* 1; Usama Sayed Mohammed2; Rehab Ragaa Nour1
1Computer Science Department, Faculty of Science, Minia University, Al Minia 61519, Egypt
2Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt
Abstract
The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG-19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image classification. 
Keywords
Image classification; Bag of visual words; Transfer learning; Convolutional neural networks; Deep learning
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