Food Calory Deteciton Using Matlab

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Description

Food Calory Deteciton Using Matlab

The Implementation of an Ingredient-Based Food recognition System using Deep learning

Objective:

            In this project main objective is  ‘To  Implementation an Ingredient-Based Food recognition System using Deep learning’


Food Calory Deteciton Using Matlab

Abstract:

            To measure the calorie of food, which are varied depending on its ingredients and volume in each cooking time, it is required to calculate the calories of food before consumption. Based on nutrition knowledge, ingredients that are components of food naturally have different calories. This paper proposes a method of ingredient-based food calorie estimation using nutrition knowledge and information. In this method, an image of the food is first recognized as a type of food, and ingredients of the recognized food are retrieved from the database with their nutrition knowledge and pattern of brightness and thermal images. Simultaneously, the image is segmented into boundaries of ingredient candidates, and all boundaries are then classified into ingredients using neural networks. Food Calory Deteciton Using Matlab


Food Calory Deteciton Using Matlab

INTRODUCTION:

Food recognition has only become a fairly popular topic in the last few years, which is largely a result of the quickly growing number of people who routinely take pictures of their food with cell phone cameras before eating it. The main application of food recognition is to create a nutritional information phone application that is able to analyze these pictures of food and deduce the nutritional content of the food eaten. The most critical and difficult step of this process is recognizing the type of food in the picture: once the type of food is determined, estimating the quantity and nutritional information is much easier. Quantity can be determined by asking participants to include a thumb in their picture or have the food a fixed distance away from the camera. Nutritional information can be looked up in official databases. Therefore, this paper addresses the problem of food recognition: determining what type of food is in the picture, given that we know that the input picture is of food and that the food is the main focus of the picture. We assume that the background is rather plain, like a plain tabletop. Even with such a restricted problem domain, food recognition is a very hard problem. Unlike other types of objects where object recognition has been more successful, such as faces, cars, and pedestrians, food is very deformable and has high intra-class variation deformable, meaning that it is amorphous. The definition of pasta has nothing to do with the shape and only has to do with its ingredients and method of preparation. Ingredients and methods of preparation manifest themselves in the visible features of shape, color, and texture. However, even in a type of food we often think of as having a rigid shape and structure, a Big Mac, there is high intra-class variation: sometimes the meat is hidden underneath the bun, sometimes the cheese isn’t visible, sometimes the lettuce is the hidden, etc. In this paper, we address these problems by trying to identify the ingredients that make up a food item. Food Calory Deteciton Using Matlab


EXISTING SYSTEM:

  1. SVM Classifier
  2. Random tree classifier
  3. K-means clustering

 DISADVANTAGES:

  1. Inaccurate results
  2. Need a large number of training datasets
  • Performance is less

PROPOSED SYSTEM:

  1. Pre-processing
  2. DWT
  3. GLCM Feature extraction
  4. Neural networks

ADVANTAGE

  • No need for manual interaction
  • Accurate results
  • Less time for the process

Food Calory Deteciton Using Matlab 

BLOCK DIAGRAM:

Food Calory Deteciton Using Matlab
Food Calory Detection Using Matlab

Software requirement:

  • Matlab 2018 B 
  • HARDWARE REQUIREMENT
  • HDD: 1TB  
  • RAM: 8GB

Food Calory Deteciton Using Matlab

REFERENCES:

  • [1] M. Bosch, F. Zhu, N. Khanna, C. Boushey, and E. Delp. Combining global and local features for food identification dietary assessment. image processing (ICIP), 201118th IEEE International Conference on, pages 1789–1792.IEEE, 2011.
  • [2] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines.ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software available athttp://www.csie.ntu.edu.tw/ chain/libsvm.
  • [3] M. Chen, K. Dhingra, W. Wu, L. Yang, R. Sukthankar, andJ. Yang. Paid: Pittsburgh fast-food image dataset. in-age Processing (ICIP), 2009 16th IEEE International Conference on, pages 289–292. IEEE, 2009.
  • [4] G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of key points. InWorkshopon statistical learning in computer vision, ECCV, volume 1, page 22, 2004.
  • [5] H. Hoashi, T. Joutou, and K. Yanai. Image recognition of 85food categories by feature fusion. multimedia (ISM), 2010IEEE International Symposium on, pages 296–301. IEEE,2010.

 

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