# Yelp Food Identification via Image Feature Extraction and Classification

arXiv: https://arxiv.org/abs/1902.05413 · HTML: https://mlsanity.com/paper/1902.05413/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning](https://mlsanity.com/paper/2203.06721/) (2022)
- [A Real-time Junk Food Recognition System based on Machine Learning](https://mlsanity.com/paper/2203.11836/) (2022)

## Abstract (arXiv)

Yelp has been one of the most popular local service search engine in US since 2004. It is powered by crowd-sourced text reviews and photo reviews. Restaurant customers and business owners upload photo images to Yelp, including reviewing or advertising either food, drinks, or inside and outside decorations. It is obviously not so effective that labels for food photos rely on human editors, which is an issue should be addressed by innovative machine learning approaches. In this paper, we present a simple but effective approach which can identify up to ten kinds of food via raw photos from the challenge dataset. We use 1) image pre-processing techniques, including filtering and image augmentation, 2) feature extraction via convolutional neural networks (CNN), and 3) three ways of classification algorithms. Then, we illustrate the classification accuracy by tuning parameters for augmentations, CNN, and classification. Our experimental results show this simple but effective approach to identify up to 10 food types from images.