TY - GEN
T1 - AIFood
T2 - 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019
AU - Lee, Gwo Giun Chris
AU - Huang, Chin Wei
AU - Chen, Jia Hong
AU - Chen, Shih Yu
AU - Chen, Hsiu Ling
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In this paper, we introduce a large-scale food images dataset namely AIFood, which is constructed to aim ingredient recognition in food image research. AIFood dataset includes 24 categories and totally 372,095 food images around the world. We collect food images from eight existing food image datasets and a food website. The food images are relabeled using 24 categories. We preliminarily label each image using existing food information, e.g. dish name or ingredient information. Next, we manually check food images to find out undiscovered ingredients and relabel them. Every image can be labeled more than one category. In addition, food images may have color cast or uneven contrast problems, which may disturb performance of image recognition system. So, we applied preprocessing method which contains automatic white balancing and contrast limited adaptive histogram equalization methods to improve visual quality of food images. We set constraints which are defined by luminance and chrominance of image to determine if the image is to be preprocessed.
AB - In this paper, we introduce a large-scale food images dataset namely AIFood, which is constructed to aim ingredient recognition in food image research. AIFood dataset includes 24 categories and totally 372,095 food images around the world. We collect food images from eight existing food image datasets and a food website. The food images are relabeled using 24 categories. We preliminarily label each image using existing food information, e.g. dish name or ingredient information. Next, we manually check food images to find out undiscovered ingredients and relabel them. Every image can be labeled more than one category. In addition, food images may have color cast or uneven contrast problems, which may disturb performance of image recognition system. So, we applied preprocessing method which contains automatic white balancing and contrast limited adaptive histogram equalization methods to improve visual quality of food images. We set constraints which are defined by luminance and chrominance of image to determine if the image is to be preprocessed.
UR - http://www.scopus.com/inward/record.url?scp=85077705104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077705104&partnerID=8YFLogxK
U2 - 10.1109/TENCON.2019.8929715
DO - 10.1109/TENCON.2019.8929715
M3 - Conference contribution
AN - SCOPUS:85077705104
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 802
EP - 805
BT - Proceedings of the TENCON 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 October 2019 through 20 October 2019
ER -