Title: A combinational convolutional neural network of double subnets for food-ingredient recognition

Authors: Lili Pan; Cong Li; Yan Zhou; Rongyu Chen; Bing Xiong

Addresses: College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, Hunan, China ' College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, Hunan, China ' College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, Hunan, China ' College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, Hunan, China ' Department of Computer and Information Sciences, Temple University, Philadelphia 19122, Pennsylvania, USA; School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, China

Abstract: Deep convolutional neural networks (DCNNs) have become the dominant machine learning for visual object recognition. They have been widely used in food image recognition and have achieved excellent performance. However, not only are the food-ingredient datasets not easy to obtain, but also the scale is not big enough to learn a deep learning model. For small-scale datasets, this paper proposes a novel DCNN architecture, which constructs an up-to-date combinational convolutional neural network of double subnets (CBDNet) for automatic classification of food ingredients using feature fusion. The feature fusion is a component which aggregates subnets for more abundant and precise deep feature extraction. In order to improve classification accuracy, some useful strategies are adopted, including batch normalisation (BN) operation and hyperparameters setting. Finally, experimental results show that the CBDNet integrating double subnets, feature fusion and BN operation extracts better image features and effectively improves the performance of food-ingredient recognition.

Keywords: food-ingredient recognition; deep feature; deep learning; deep convolutional neural network; DCNN.

DOI: 10.1504/IJES.2020.110658

International Journal of Embedded Systems, 2020 Vol.13 No.4, pp.439 - 448

Received: 27 Mar 2019
Accepted: 02 Sep 2019
Published online: 27 Oct 2020 *

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