TY - GEN
T1 - Mixed Dish Recognition through Multi-Label Learning
AU - Wang, Yunan
AU - Chen, Jing-jing
AU - Ngo, Chong-Wah
AU - Chua, Tat-Seng
AU - Zuo, Wanli
AU - Ming, Zhaoyan
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2019/6
Y1 - 2019/6
N2 - Mix dish recognition, whose goal is to identify each of the dish type presented on one plate, is generally regarded as a difficult problem. The major challenge of this problem is that different dishes presented in one plate may overlap with each other and there may be no clear boundaries among them. Therefore, labeling the bounding box of each dish type is difficult and not necessarily leading to good results. This paper studies the problem from the perspective of multi-label learning. Specially,we propose to perform dish recognition on region level with multiple granularities. For experimental purpose, we collect two mix dish datasets: mixed economic rice and economic beehoon. The experimental results on these two datasets demonstrate the effectiveness of the proposed region-level multi-label learning methods.
AB - Mix dish recognition, whose goal is to identify each of the dish type presented on one plate, is generally regarded as a difficult problem. The major challenge of this problem is that different dishes presented in one plate may overlap with each other and there may be no clear boundaries among them. Therefore, labeling the bounding box of each dish type is difficult and not necessarily leading to good results. This paper studies the problem from the perspective of multi-label learning. Specially,we propose to perform dish recognition on region level with multiple granularities. For experimental purpose, we collect two mix dish datasets: mixed economic rice and economic beehoon. The experimental results on these two datasets demonstrate the effectiveness of the proposed region-level multi-label learning methods.
KW - Mix dish recognition
KW - Multi-label recogniition
KW - Multiscale
KW - Region-wise
UR - http://www.scopus.com/inward/record.url?scp=85068007278&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85068007278&origin=recordpage
U2 - 10.1145/3326458.3326929
DO - 10.1145/3326458.3326929
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450367790
T3 - CEA - Proceedings of the Workshop on Multimedia for Cooking and Eating Activities
SP - 1
EP - 8
BT - CEA'19
PB - Association for Computing Machinery
T2 - 11th Workshop on Multimedia for Cooking and Eating Activities (CEA'19)
Y2 - 10 June 2019
ER -