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 | ||
|
Statistics Article View: 350 PDF Download: 248 |
||
