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

Document Type : Original Article


1 Computer Science Department, Faculty of Science, Minia University, Al Minia 61519, Egypt

2 Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt


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. 


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