Yelp Food Identification via Image Feature Extraction and Classification
This work addresses the inefficiency of manual labeling for food photos on Yelp, offering a practical solution for restaurant customers and business owners, though it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of automatically identifying food types from Yelp photos, which currently relies on human labeling, by developing a machine learning approach that achieved classification accuracy for up to 10 food types using image preprocessing, CNN feature extraction, and classification algorithms.
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.