https://github.com/tkd-alex/food-classification

A Social Media Management project. Feature collection with BOW. Pics classification with k­-NN.
https://github.com/tkd-alex/food-classification

bovw classification feature food knn learning machine python

Last synced: 11 months ago
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A Social Media Management project. Feature collection with BOW. Pics classification with k­-NN.

• Host: GitHub
• URL: https://github.com/tkd-alex/food-classification
• Owner: Tkd-Alex
• Created: 2017-03-13T08:21:07.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2018-05-06T08:14:29.000Z (about 8 years ago)
• Last Synced: 2025-04-05T05:41:19.040Z (about 1 year ago)
• Topics: bovw, classification, feature, food, knn, learning, machine, python
• Language: Python
• Size: 753 KB
• Stars: 7
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Food-Classification

This _Social Media Management_ project allow to classificate picture between food and non-food.

#### What we do:

1. Extract feature splitted 70% for training and 30% test set.

2. With deasy (step=8) extract local feature.

3. To build aur dictionary of visual word, we have cluster all local feature with KMeans=500 (scikitlearn).

4. Finally to classificate the picture we have used KNN.

![markdown-preview](doc/Schermata%20da%202017-03-13%2009-33-58.png)

## Bag of visual word

>In computer vision, the bag-of-words model (BoW model) can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.

### Set enviroment

You need to have install on your system:

- [Python2.7](http://www.python.it/) 

- [Skimage](http://scikit-image.org/)

- [Numpy](http://www.numpy.org/)

- [Matplotlib](http://matplotlib.org/)

### How to use

To use this script you need to clone this repo and download from release the **dataset.7z** file.

Extract the file in the same folder of the repo and extract _dataset/Food.7z_ & _dataset/Non-Food.7z_.

Launch _main.py_ and enojy the classificator.

### References

Original dataset:

- [Food Dataset](http://iplab.dmi.unict.it/madima2015/UNICT-FlickrFood.rar)

- [Non-Food Dataset](http://iplab.dmi.unict.it/madima2015/UNICT-FlickrNon-Food.rar)

Inspired by http://iplab.dmi.unict.it/madima2015