Food Category Representatives: Extracting Categories from Meal Names in Food Recordings and Recipe Data
Food Category Representatives: Extracting Categories from Meal Names in Food Recordings and Recipe Data
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DOI：
10.1109/bigmm.2015.54
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
Sosuke Amano;K. Aizawa;Makoto Ogawa
中科院分区：
文献类型：
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作者：
Sosuke Amano;K. Aizawa;Makoto Ogawa
Food Log是一个多媒体记录工具，用于为许多人生成食物记录。在运营的一年时间里，Food Log已经为用户提供了超过100万份食物记录。我们在这些数据中发现了近7万条独特的食物记录。在分析它们时，挑战之一是从如此大量的记录中提取膳食类别。在本文中，我们提出了一种方法，用于压缩成一个较短的表示餐名。首先，我们使用k-最近邻搜索收集相似的膳食名称。接下来，我们构建一个词图来建模数据库中的餐食名称和项目之间的关系。我们通过识别词图中的最小路径来选择代表性词。最后，我们得到了一些词，代表了有关原始膳食名称的分类信息。我们将该方法应用于Food Log和Rakuten Recipe数据库的食品记录中的数据。我们的研究结果表明，该方法有效地为这两个数据集。
Food Log is a multimedia recording tool for producing food records for many individuals. In one year of operation, Food Log has produced more than one million food records for meals eaten by users. We found nearly 70,000 unique food records among these data. In analyzing them, one of the challenges is to extract meal categories from such a large number of records. In this paper, we propose a method for compressing a meal name into a shorter representation. First, we collect similar meal names using a k-nearest neighbor search. Next, we construct a word graph to model the relationship between the meal names and items in the database. We select representative words by identifying minimal paths in the word graph. Finally, we obtain a few words that represent categorical information about the original meal name. We applied the method to data in food records for both Food Log and the Rakuten Recipe database. Our results show that the method worked effectively for both datasets.