A Comparison of Several Approaches for Image Recognition used in Food Recommendation System

Quang-Linh Tran, Gia-Huy Lam, Quang-Nhat Le, T. Tran, Trong-Hop Do
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引用次数: 2

Abstract

In food recommendation systems, users can use mobile devices to capture images of the dishes they eat. The types of dishes in images will be automatically recognized and input to the recommendation system to suggest other dishes which the users likely to enjoy. Food image recognition is therefore an essential part of the food recommendation system. This used to be a hard problem in computer vision as many foods are very similar in color and texture. Thanks to the development of artificial intelligent and especially deep learning techniques, it is much easier to build a program to recognize the type of foods in the image. This paper examines several approaches, from traditional machine learning to state-of-the-art deep learning techniques for food image recognition to provide a comparison of the performance of these techniques. To this end, a new dataset of Vietnamese cuisine including 12,017 photos of 15 dishes has been built to test algorithms. Traditional machine learning techniques including Histogram of Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) and state-of-the-art deep learning models including VGG16, MobileNet, ANN, Resnet18, Resnet50, Densenet121 have been used for extracting features in the food images. Logistic Regression (SF) and SoftMax (SM) classification have been used for classification using the extracted features. Based on the comparison results provided in this paper, one can choose appropriate techniques for image recognition to build a good food recommendation system.
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食品推荐系统中几种图像识别方法的比较
在食物推荐系统中,用户可以使用移动设备捕捉他们所吃菜肴的图像。图像中的菜肴类型将被自动识别并输入到推荐系统中,以推荐用户可能喜欢的其他菜肴。因此,食品图像识别是食品推荐系统的重要组成部分。这曾经是计算机视觉中的一个难题,因为许多食物在颜色和质地上非常相似。由于人工智能的发展,特别是深度学习技术的发展,构建一个程序来识别图像中的食物类型要容易得多。本文研究了几种方法,从传统的机器学习到最先进的食品图像识别深度学习技术,以提供这些技术性能的比较。为此,已经建立了一个新的越南美食数据集,其中包括15种菜肴的12017张照片,以测试算法。传统的机器学习技术包括梯度直方图(HOG)和尺度不变特征变换(SIFT),以及最先进的深度学习模型包括VGG16、MobileNet、ANN、Resnet18、Resnet50、Densenet121已被用于提取食物图像中的特征。使用逻辑回归(SF)和SoftMax (SM)分类对提取的特征进行分类。根据本文提供的对比结果,可以选择合适的图像识别技术来构建一个好的食物推荐系统。
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