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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.

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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 

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