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, Page 1-6 PDF (396.99 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/kjis.2021.198376 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Abdelmgeid Ali  1; Usama Sayed Mohammed2; Rehab Ragaa Nour1
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| 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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