Abstract-The The image is processed – either

 

Abstract-The
Increase in number of diabetic patients in day to day life due to lack of proper
diet tends us to sought out a solution for it. Food Image Classification is a technique where a classifier assigns to the image one class out
of a pre-defined set of food classes, a set of characteristics representing the
visual content of the image is extracted and quantified. We use Bag-of-Features model for automatic selection
of food images, BoF methods are based on
the order less collections of quantized local image descriptors, they discard
spatial information and are conceptually and computationally simpler than many
alternative methods. Image classification undergoes key point extraction, builds
a visual dictionary by using k-means clustering and then finally classifies the
food image into various classifiers.

Keywords-Bag of features(BoF), Image classification, Key point
extraction, k-means clustering.

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INTRODUCTION

The
Tremendous increase in number of diabetic patients worldwide, due to their
proven inability to assess their diet properly, raised the need to develop
systems that will support type 1 diabetic (T1D) patients during CHO counting.
The increasing recent advances made in computer vision, permitted the
introduction of image analysis-based applications for diet management. In a
typical scenario, the user acquires an image of the upcoming meal using the
camera of his phone. The image is processed – either locally or on the server
side – in order to extract a series of features describing its visual
properties. The extracted features are fed to a classifier to recognize the
various food types of the acquired image, which will then be used for the CHO
estimation.

The
scope of this experiment is to identify the proper descriptor size or
combination of sizes that should be used to describe the best performing key
point extraction technique. To this end, different sizes were evaluated and
then combined into a multi-scale scheme using a dense sampler. The used
descriptor sizes were 16, 24, 32 and 56 all their combinations with spacing
among them equal to 1/2 of each size in order to guarantee a sufficient number
of patches.

The existing image analysis context, an image is represented by
the histogram of visual words, which are defined as representative image
patches of commonly occurring visual patterns. The BoW model is a simplifying representation used in natural
language processing and information retrieval. One aim of BoW is to categorize documents, which ignores the order
of the words belonging to a previously defined word dictionary and considers
only how frequently they appear. The concept of the BoF model
adequately fits the food recognition problem, since a certain food type is
usually perceived as an Ensemble of different visual elements mixed with
specific proportions, but without any typical spatial arrangement, a fact that
encourages the use of a BoF  approach,
instead of any direct image matching
technique.

Existing Technique :BoW

Drawbacks of Existing System:

Key points extract from is not such easy task. The
data’s size and complexity and the variability in content

SURVEY

We propose a benchmark of several objective
functions for large-scale image classification1. Image category selection is
important to access visual information on the level of objects and scene
types2. The local descriptors are hierarchically quantized in a vocabulary
tree which allows a larger discriminatory vocabulary to used efficiently6.
Recently Bag-of-Features model had  became
more popular for content based image classification with better performance and
simplicity7. We treat images as collections of independent patches and then
sampling the set of patches7. Evaluating the visual dictionary for each
individual path independently and using the resulting samples in Image
classification7. We use color histogram and bag of SIFT features to
discriminate classifier8. Recently a bag-of-features model was introduced
into the area of computer vision as a global image descriptor for difficult
classifications9. BoF was derived from the bag-of-words (BoW) model. BoW
model is a popular way of representing documents in natural language
processing10. A visual dataset with nearly 5000 homemade food images was
created, reflecting the nutritional habits11. The concept of BoF model
adequately fits the food selection problem 
because a certain food item is usually perceived as an ensemble of
different visual elements mixed with specific proportions without any spatial
arrangement10.

 

PROPOSED SYSTEM OBJECTIVE

The
image classification stage is involved in both training and testing phases. In
order to identify the appropriate classifier for the specific problem.
Identifying the appropriate descriptor size and type for a recognition problem
is a challenging task that involves a number of experiments. Its Inability to
capture any colour information constitutes a problem for the description of
many Objects, including foods. We analyse the problem of clustering food image
data list based RGB values

Algorithm:
k-means clustering, the k-means
clustering is an iterative algorithm in which objects are moved among set of
clusters until the desired set is achieved. It is most popular and commonly
used method. The algorithm is built on the concept of user specified input
parameter (k). A set of n objects are divided into k clusters by the algorithm.
A high degree of similarity among elements in clusters is obtained. Key points
are selected points on an image that define the centres of local patches where
descriptors will be applied. In the current study, three different key point
extraction methods were tested: interest Point detectors, random sampling and
dense sampling. Interest point detectors, such as SIFT are considered as the
best choice for image matching problems where a small number of samples is
required, as it provides stability under local and global image perturbations.

Input:
A data set containing n objects, number of desired clusters k.

Output:
A set consisting of k clusters

Advantages of k-means areDecision
trees and neural networks were helpful to generate binary classifiers of images
and After the key point extraction, a local image descriptor is applied to a
rectangular area around each key point.

 

IMPLEMENTATION

AUTHENTICATION

  In authentication module is used to checking
the user as valid or invalid. In this module enter the username and password,
this username and password is check into the database. If username and password
is correct then allow to next processing, otherwise it consider as invalid user
and again go to the login process.

 

DATASET COLLECTION

In
this module we describe the image dataset that it is contain various types of
food images. The overall goal of the task is to
collecting multi-modal images approaches that combine textual and visual
evidence in order to satisfy a user’s multimedia information need could deal
with larger scale image collections that contain highly heterogeneous items
both in terms of their textual descriptions and their visual content.

KEYPOINT EXTRACTION

Key
points are selected points on an image that define the centers of local patches
where descriptors will be applied. In the current study, three different key
point extraction methods were tested: interest point detectors, random sampling
and dense sampling. Interest point detectors, are considered as the best choice
for image matching problems where a small number of samples is required, as it
provides stability under local and global image perturbations.

FEATURE DESCRIPTION

A
local image descriptor is applied to a rectangular area around each key point
to produce a feature vector. Identifying the appropriate descriptor size and
type for a recognition problem is a challenging task that involves a number of
experiments.

 DESCRIPTOR QUANTIZATION

Descriptor
quantization is the procedure of assigning a feature vector to the closest
visual word of a predefined visual vocabulary. Once the visual dictionary is
learnt, each descriptor of an image is quantized and the histogram of visual
word occurrences serves as a global description of the image. Then, the
histogram values are usually scaled to 0 1 and are fed to the classifier either
for training or testing. The efficiency of this part of the system is crucial,
since it affects processing times for both training and testing. The complexity
of the descriptor quantization mainly depends on the dimensions of the
descriptor and the number of visual words

 IMAGE CLASSFICATION

The
image classification stage is involved in both training and testing phases. In
order to identify the appropriate classifier for the specific problem, several
experiments with three supervised classification methods were conducted.
According to our food type we can those images for further more easy way to
retrieving images from classified lists.

 

                           

 

 

 

 

 

 

 

 

 

 

RESULTS

The
above fig illustrates the design for uploading images from food list dataset and
it shows the results about uploaded images

Design
for Admin Work for Image Classification

The above fig illustrates
the design for SIFT sampling technique which will use to key point extraction.

The above fig illustrates
the design for DENSE sampling technique which will use to key point extraction.                                                       

 The above fig illustrates the design for RANDOM
sampling technique which will use to key point extraction

 

Design
for Feature Description Details.

 The above fig illustrates the design for feature
description details; it contains the details about RGB values about uploaded
images. It
explains the feature description module

 Design for Adding Image in Food List Items

 The above fig illustrates the design for
adding image in food list items after predicting uploaded image results.

CONCLUSION

The
final, optimized system achieved overall recognition accuracy in the order of
78%, proving the feasibility of a BoF-based system for the food recognition
problem. For future work, a hierarchical approach will be investigated by
merging visually similar classes for the first levels of the hierarchical
model, which can then be distinguished in a latter level by exploiting
appropriate discriminative features.

FUTURE ENHANCEMENT

Hierarchical
approach will be investigated by merging visually similar classes for the first
levels of the hierarchical model, which can then be distinguished in a latter
level by exploiting appropriate discriminative features. Moreover, the
enhancement of the visual dataset with more images will improve the
classification rates, especially for the classes with high diversity.

 

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