|
| 论文编号: |
|
| 第一作者所在部门: |
|
| 中文论文题目: |
Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration
|
| 英文论文题目: |
Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration
|
| 论文题目英文: |
|
| 作者: |
Min, Weiqing
|
| 论文出处: |
|
| 刊物名称: |
IEEE TRANSACTIONS ON MULTIMEDIA
|
| 年: |
2017
|
| 卷: |
19 |
| 期: |
5 |
| 页: |
1100-1113 |
| 联系作者: |
|
| 收录类别: |
|
| 影响因子: |
|
| 摘要: |
This paper considers the problem of recipe-oriented image-ingredient correlation learning with multi-attributes for recipe retrieval and exploration. Existing methods mainly focus on food visual information for recognition while we model visual information, textual content (e.g., ingredients), and attributes (e.g., cuisine and course) together to solve extended recipe-oriented problems, such as multimodal cuisine classification and attributeenhanced food image retrieval. As a solution, we propose a multimodal multitask deep belief network (M3TDBN) to learn joint image-ingredient representation regularized by different attributes. By grouping ingredients into visible ingredients (which are visible in the food image, e.g., "chicken" and "mushroom") and nonvisible ingredients (e. g., "salt" and "oil"), M3TDBN is capable of learning both midlevel visual representation between images and visible ingredients and nonvisual representation. Furthermore, in order to utilize different attributes to improve the intermodality correlation, M3TDBN incorporates multitask learning to make different attributes collaborate each other. Based on the proposed M3TDBN, we exploit the derived deep features and the discovered correlations for three extended novel applications: 1) multimodal cuisine classification; 2) attribute-augmented cross-modal recipe image retrieval; and 3) ingredient and attribute inference fromfood images. The proposed approach is evaluated on the constructed Yummly dataset and the evaluation results have validated the effectiveness of the proposed approach. |
| 英文摘要: |
|
| 外单位作者单位: |
|
| 备注: |
|
关闭窗口 |
|
