  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.
