Image Classification in Arabic: Exploring Direct English to Arabic Translations

Image classification is an ongoing research challenge. Most of the current research focuses on image classification in English with very little research in Arabic. Expanding image classification to Arabic has several applications and benefits. This paper investigates the accuracy of direct translations of English labels that are available in ImageNet, a database of images labeled in English that is commonly used in computer vision research, to Arabic. A dataset comprised of 2,887 labeled images was constructed by randomly selecting images from ImageNet. All of the labels were translated to Arabic using an online translation service. The accuracy of each translation was evaluated by a human judge. Results indicated that 65.6% of the generated Arabic labels were accurate with the highest results achieved when the labels consisted of only one word. This study makes three important contributions to the image classification literature: (1) it determines a baseline level of accuracy for image classification in Arabic algorithms; (2) it provides 1,910,935 images classified with accurate Arabic labels (based on accurately labeling 1,895 images that consist of 1,643 unique synsets); and (3) it measures the accuracy of translations of image labels in ImageNet to Arabic.