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ENGR 295A Proposal Template








从以前的工作中,我们发现食品图像识别中使用的机器学习技术已经发展了多年。在使用深度学习技术之前,人们使用诸如支持向量机之类的传统方法。当卷积神经网络于2014年问世时,它在图像处理项目中显示出巨大的潜力,Kagaya [1]首先将CNN纳入了食品图像识别,其结果超过了以前的方法,将准确率从60%提高到90%以上。

2.2 Improved training methods would strengthen the ability of Food Image Detection on multiple types of food

Training a large Convolutional Neural Network has always been a big issue which stops people from applying the powerful deep learning techniques in more fields. To solve the problem of computational intenseness, many different transfer learning methods have been involved. Based on open-source large-scale image datasets, Maxime Oquab[2] developed methods to build CNN on specific domains without large quantities of images available.

Another advantage of transfer learning is that recent work of Kabir[3] shows that machine learning algorithms can benefit from data of different domains. Based on the discovery, it’s worth testing tuning some well-trained CNN models on food image datasets, even though the underlying data is of other specific fields such as human faces.

Due to the lack of high-quality food images and the limitation of computation power, Kagaya’s CNN model can only recognize 10 types of food, which is far from enough if we are going to put the model into practical use such as being implemented in diet APPs. We assume that by introducing transfer learning and well-trained CNN models from other domains, our new model could outweigh the former one.

4 Feasibility Study/Analysis (Kate)
same as we did in ENGR 202 system engineering
4.1 Subsection
4.2 Subsection
5 Project Concepts and High Level Architectures (Samantha)
block diagrams, high level architecture (your own – CNN, volume estimator, calorie calculator)

4.1 Project Concepts. We accomplish system planning and architecting in response to the customer needs. Our project requirements are to be able to detect fast food images, to accurately measure daily food intake, and to provide a detailed food profile with nutrition and calories information. Our project’s functional requirements have a three-step process, such as extracting regions of interests (ROIs), classifying different food categories, and generating a dietary assessment report. System Operational Requirements are for the first-step process, apply Region Proposal Network (RPN) which is derived from Faster R-CNN model to separate food items from the image background, for the second-step process, apply deep Convolutional Neural Network (CNN) on selected ROIs for image processing, segmentation, and classification into different food categories, and for the third-step process, use dietary assessment tools to provide nutrition analysis report. For Functional Architecture, we will ensure that our application will satisfy the software requirements’ specifications and stakeholders’ needs and expectations.

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