Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation

被引:141
作者
Kawano, Yoshiyuki [1 ]
Yanai, Keiji [1 ]
机构
[1] Univ Electrocommun, Dept Informat, Chofu, Tokyo 1828585, Japan
来源
COMPUTER VISION - ECCV 2014 WORKSHOPS, PT III | 2015年 / 8927卷
关键词
Dataset expansion; Food image; Foodness; Domain adaptation; Crowd-sourcing; Adaptive SVM;
D O I
10.1007/978-3-319-16199-0_1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a novel effective framework to expand an existing image dataset automatically leveraging existing categories and crowdsourcing. Especially, in this paper, we focus on expansion on food image data set. The number of food categories is uncountable, since foods are different from a place to a place. If we have a Japanese food dataset, it does not help build a French food recognition system directly. That is why food data sets for different food cultures have been built independently so far. Then, in this paper, we propose to leverage existing knowledge on food of other cultures by a generic "foodness" classifier and domain adaptation. This can enable us not only to built other-cultured food datasets based on an original food image dataset automatically, but also to save as much crowd-sourcing costs as possible. In the experiments, we show the effectiveness of the proposed method over the baselines.
引用
收藏
页码:3 / 17
页数:15
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