The document discusses a machine learning project that uses image processing, segmentation, and classification to recognize foods in images and calculate their calorie and nutrition content. The proposed system takes pictures of food as input and analyzes them to detect the type of food and measure its portions to accurately calculate calories and nutritional energy levels. This approach provides an effective way for dietitians and patients to analyze daily food intake and manage health. The system is designed to improve current calorie measurement techniques.

1. FOOD CALORIE RECOGNITION AND
CLASSIFICATION USING MACHINE LEARNING
Project Domain : Machine Learning
STUDENT NAME Project Supervisor:
Reg no:XXXXXX NAME,
Designation

2. ABSTRACT
• Food is one of the most important requirements of every living being on earth. The human beings
require their food to be fresh, pure and of standard quality.
• The standards imposed and automation carried out in food processing industry takes care of food
quality.
• Now a day, people across the universe are becoming more sensitive to their diet. Unbalanced diet may
cause many problems like weight gain, obesity, diabetes, etc.
• So different systems were developed so as to analyze food images to calculate calorie and nutrition
level.
• This system proposes a effective way to measure and manage daily food intake of patients and
dietitians.
• The system will take the images of food and using image processing, segmentation and classification it
calculates the nutrition and calorie content in the food.

3. ABSTRACT CONT..
• The proposed system will certainly improve and facilitate the
current calorie measurement techniques.
• In this project, food portion recognition system use for measuring
the calorie and nutrition values.
• The user just to take a picture of the food image then to recognize
the image to detect the type of food portion and classify using
support vector machine.
• We are performing segmentation, food portion recognition using
skull striping and classification using support vector machine to
calculate the calorie along with the type of energy in accurate way.

4. INTRODUCTION TO DOMAIN
• MACHINE LEARNING:
• In the statistical context, Machine Learning is defined as an application of artificial
intelligence where available information is used through algorithms to process or
assist the processing of statistical data.
• While Machine Learning involves concepts of automation, it requires human
guidance.
• Machine Learning involves a high level of generalization in order to get a system that
performs well on yet unseen data instances.
• Machine learning is a relatively new discipline within Computer Science that
provides a collection of data analysis techniques.
• Some of these techniques are based on well established statistical methods (e.g.
logistic regression and principal component analysis) while many others are not.

5. INTRODUCTION TO PROJECT
• Calorie is a measuring unit which is defined as the amount of heat energy needed to raise the
temperature of one gram of water by one degree.
• The process of providing or obtaining the food necessary for health and growth is called Nutrition.
This unit is commonly used to measure the overall amount of energy in any food portion that consists
of the main food components of Carbohydrate, Protein and Fat.
• Calories are a must for the body, as they are generate energy. But it is said that an excess of anything is
bad and the same applies to the intake of calories too.
• If there is an excess of calories in our body, it gets stored in the form of fats, thus making us
overweight.
• Adult calorie requirements differ from that of a child and in the same way, the daily calorie
requirement of an Body Mass Index is a person’s weight in kilograms divided by the square of their
height in meters.

6. OBJECTIVE OF PROJECT
• Less noise ratio.
• Increase the efficiency in predicting the food calorie correctly.
• Less time consumption in prediction.
• User friendly and should be applicable to all foods like fruits,
vegetables and hotel foods.
• More accuracy rate.

7. EXISTING SYSTEM
• Thumb calibration:
• There is another system which is based on support vector machine but use the
thumb for calibration of each and every food image but it require long calculation
for measuring nutrition that measurement system also uses a photo of the food,
taken with the camera of a smart phone, but uses the thumb of patient for
calibration, which solves the problem of carrying cards or special trays.
• More specifically, an thumb image is captured and stored with its measurements
in the first usage time (first time calibration).
• Now, this unique method will lead to relatively accurate results without the
difficulties of other methods.
• Food images will then be taken with the user’s thumb placed next to the dish,
make it easy to measure the real life size of the portions.

8. DISADVANTAGE OF EXISTING SYSTEM
1. Poor image resolution
2. Less Accuracy
3. Poor lighting and low contrast
4. Higher Computational Cost
5. Lack of standards
6. More time consuming process

9. PROBLEM STATEMENT
• Not Economical
• More time consuming process.
• Early prediction of food calorie is not possible.
• Less accuracy rate
• Cannot be implemented in all images and videos.

10. PROPOSED SYSTEM
Support Vector Machine:
• The methodology consists of two main parts first one is segmentation using fuzzy c means
and second one is classification by SVM.
• These important steps are described below architecture.
• Segmentation is a process of extracting and representing information from an image is to
group pixels together into regions of similarity Segmentation subdivides an image into its
constituent regions or objects that have similar features according to a set of predefined
criteria.
• The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions
corresponding to individual surfaces, objects, or natural parts of objects.
• A segmentation could be used for object recognition, occlusion boundary estimation within
motion or stereo systems, image compression, image editing, or image database look-up.

11. PROPOSED SYSTEM ADVANTAGES
• High output efficiency.
• User friendly.
• Less time consumption.
• User friendly.
• Segmented areas of activity can be recognized correctly.

12. ARCHITECTURE DIAGRAM

13. MODULES DESCRIPTION
a. Image Acquisition
b. Pre-processing
c. Segmentation
d. Feature Extraction
e. Classification

14. MODULES DESCRIPTION
1. Image Acquisition Image
• Acquisition is the process of collection of images. These images are downloaded
from the online dataset provider called Kaggle.com.
2. Image Preprocessing
• Image preprocessing includes converting RBG images into Grayscale images. An
RGB image means the images present with its original colors.
• Grayscale images have the combination of black and white. Conversion of RGB
to grayscale is done for enhancing the dataset available. Converting the images to
grayscale helps in improving the accuracy of the result.
• Grayscale images help to reduce noise and also make the background neutral. It
also helps to improve brightness of the image.
• Data augmentation is a way of creating new data which has benefits like the
ability to generate more data from limited data and it prevents over fitting.

15. MODULES DESCRIPTION
3. Image Segmentation
• Image segmentation breaks the image down into meaningful
regions.
• It divides digital image into multiple segments. The goal is to
simplify or change the representation into more meaningful image.
• It differentiates between the objects we want to inspect further and
the other objects or their background.
• It consists of segmenting the converted grayscale images using K
means segmentation.

16. MODULES DESCRIPTION
4. Feature Extraction
• Feature extraction is extracting or showing of the segmented portion
of the image so that classification becomes easy.
• Features are extracted in order to differentiate between the images.
• Features extraction is used in almost all machine vision algorithms.
• The common goal of feature extraction and representation
techniques is to convert the segmented objects into representations
that better describe their main features and attributes.
• Here, shape of the spots of the land area is extracted.

17. MODULES DESCRIPTION
5. Classification
• Here we use the concept of SVM algorithm. The last module includes the
classification in which Tensor Flow and Machine Learning algorithm will
be used.
• Tensor Flow is a python-friendly open source library for numerical
computation that makes machine learning faster and easier.
• Tensor Flow allows developers to create dataflow graphs - structures that
describe how data moves through a graph, or a series of processing nodes.
• Each node in the graph represents a mathematical operation, and each
connection or edge between nodes is a multidimensional data array, or
tensor.

18. HARDWARE REQUIREMENTS
• Processor: Intel Dual core.
• Ram: 4 GB RAM
• Hard Disk: 500 G.B Hard Disk
• 14 inch monitor

19. SOFTWARE REQUIREMENTS
• Technology : Matlab 2018
• Web Technologies : Html, JavaScript, CSS
• IDE : Matlab IDE
• Database : My SQL

20. CONCLUSION
• In the implementation of food recognition system based on image processing the
comparative study of various software scheme is done.
• we proposed a measurement method that estimates the amount of calories from a
food’s image by measuring the area of the food portions from the image and using
nutritional facts tables to measure the amount of calorie and nutrition in the food.
• And calorie is shown in final results with approximate value.
• Thus the project is designed to aid dieticians for the treatment of obese or overweight
people, although normal people can also benefit from our system by controlling more
closely their daily eating without worrying about overeating and weight gain.
• This is simple and easy to use. Hence this system is very important in the field of
biomedical, the actual program is clear and easy to False Negative (FN) - Low
energious is incorrectly understand.

21. REFERENCES
1. Http://www.noo.org.uk/uploads/doc789_40_noo_bmi.pdf
2. World health organization. (2011, october) obesity study.[online].
Http://www.who.int/mediacentre/factsheets /fs311/en/index.html
3. World health organization. (2012) world health statistics 2012.[online].
Http://www.who.int/gho/publications/world_health_statistics/2012/ en/index.html
4. Parisa Pouladzadeh, Shervin Shirmohammadi and Rana Almaghrabi, "Measuring
Calorie and Nutrition from Food Image" Food Recognition -IEEE-TIM-final.rar
,2014
5. George a. Bray and claude bouchard, handbook of obesity, second edition, ed.
Louisiana, usa: ennington biomedical research center, 2004.
6. Geeta shroff, asim smailagic, “Neural Network based Food Recognition and
Calorie calculation for diabetes patients”, diawear technical report, pp. 1-8,
march, 2009.