Cloud-Based Mobile Platform for Chinese Food Identification and Menu Character Recognition
Date Issued
2014
Date
2014
Author(s)
Tsai, Yi-Ting (Evelyn)
Abstract
Foreigners in Chinese-speaking countries of Asia often face a problem with eating out. They neither recognize the foods nor can read the menus about them, making first-timers confused about what to eat and what they are eating. Food recognition is a topic of research that has received increasing attention due to the rising concern for health and its cause-and-effect relationship to nutrition and diet. A computer-aided tool for food recognition that can allow people to know what they are eating is a proper solution to this issue. Here we propose a system that identifies a food item by its characteristic features, and is also able to recognize Chinese words on menus. It utilizes SIFT and local binary pattern with sparse coding, Gabor and color features as descriptors for a particular food item. We use SVM classifier for training each feature. Adaboost algorithm is applied to perform evaluations of each feature or descriptor of a food item and to assign a weight to that feature. A data bank of pictures of 67 food items, at least 100 images for each of them and collected from internet search or direct photographing of our meals, is constructed for system testing. Another function of menu character recognition is achieved with Google’s Tesseract optical character recognition (OCR), which analyzes and extracts texts from images and compares them to the names of food items in the data bank to find the best match. Chinese dishes are often named according to the style of cooking, the primary and ingredients used, and other descriptive words; in addition; the main ingredient is often placed at the end of a name. Therefor we also incorporate a language model composed of a semantic network of names of foods in Traditional Chinese. To increase accuracy, we designate differing weights to each character in a dish name according to the naming pattern in Chinese dishes. Our databank contains more than 123 dish names with differing weights and semantic relations between each character in the names. The recognition results will show the results of recognition of food image or food name; in addition, we will also show the food’s ingredients, nutrition information (calories, vitamins, lipids), cooking style, in both English and Chinese languages. The results show that computation time for recognition in both food and menu is two to three times less using our system, compared to that required using Google Image and Google Translate (t = 2.45). Overall user satisfaction is 80.45% for our food recognition system and 83.41% for menu recognition system. Finally, this system uses cloud computing and parallel computing to accelerate computation on a mobile platform.
Subjects
食物辨識
菜單辨識
特徵描速
分類器
光學字元辨識
平行運算
Type
thesis
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