TY - JOUR
T1 - Image classification in Arabic
T2 - Exploring direct english to Arabic translations
AU - Alsudais, Abdulkareem
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Image classification
KW - Image processing
KW - Information retrieval
KW - Machine translation
KW - Natural language processing
UR - http://www.scopus.com/inward/record.url?scp=85078320907&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2926924
DO - 10.1109/ACCESS.2019.2926924
M3 - Article
AN - SCOPUS:85078320907
SN - 2169-3536
VL - 7
SP - 122730
EP - 122739
JO - IEEE Access
JF - IEEE Access
M1 - 8755849
ER -