Open Access

ARTICLE

Image Augmentation-Based Food Recognition with Convolutional Neural Networks

Lili Pan¹, Jiaohua Qin^1,*, Hao Chen², Xuyu Xiang¹, Cong Li¹, Ran Chen¹

1 College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China.
2 College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.

* Corresponding Author: Jiaohua Qin. Email: .

Computers, Materials & Continua 2019, 59(1), 297-313. https://doi.org/10.32604/cmc.2019.04097

Abstract

Image retrieval for food ingredients is important work, tremendously tiring, uninteresting, and expensive. Computer vision systems have extraordinary advancements in image retrieval with CNNs skills. But it is not feasible for small-size food datasets using convolutional neural networks directly. In this study, a novel image retrieval approach is presented for small and medium-scale food datasets, which both augments images utilizing image transformation techniques to enlarge the size of datasets, and promotes the average accuracy of food recognition with state-of-the-art deep learning technologies. First, typical image transformation techniques are used to augment food images. Then transfer learning technology based on deep learning is applied to extract image features. Finally, a food recognition algorithm is leveraged on extracted deep-feature vectors. The presented image-retrieval architecture is analyzed based on a small-scale food dataset which is composed of forty-one categories of food ingredients and one hundred pictures for each category. Extensive experimental results demonstrate the advantages of image-augmentation architecture for small and medium datasets using deep learning. The novel approach combines image augmentation, ResNet feature vectors, and SMO classification, and shows its superiority for food detection of small/medium-scale datasets with comprehensive experiments.

---

Keywords

---

Citations