Lightweight Food Recognition via Aggregation Block and Feature Encoding

doi:10.1145/3680285

	Lightweight Food Recognition via Aggregation Block and Feature Encoding
	Yang, Yancun 1; Min, Weiqing 2; Song, Jingru 1; Sheng, Guorui 1; Wang, Lili 1; Jiang, Shuqiang 2
	2024-10-01
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
卷号	20 期号:10 页码:25
摘要	Food image recognition has recently been given considerable attention in the multimedia field in light of its possible implications on health. The characteristics of the dispersed distribution of ingredients in food images put forward higher requirements on the long-range information extraction ability of neural networks, leading to more complex and deeper models. Nevertheless, the lightweight version of food image recognition is essential for improved implementation on end devices and sustained server-side expansion. To address this issue, we present Aggregation Feature Net (AFNet), a lightweight network that is capable of effectively capturing both global and local features from food images. In AFNet, we develop a novel convolution based on a residual model by encoding global features through row-wise and column-wise information integration. Merging aggregation block with classic local convolution yields a framework that works as the backbone of the network. Based on the efficient use of parameters by the aggregation block, we constructed a lightweight food image recognition network with fewer layers and a smaller scale, assisted by a new type of activation function. Experimental results on four popular food recognition datasets demonstrate that our approach achieves state-of-the-art performance with higher accuracy and fewer FLOPs and parameters. For example, in comparison to the current state-of-the-art model of MobileViTv2, AFNet achieved 88.4% accuracy of the top-1 level on the ETHZ Food-101 dataset, with similar parameters and FLOPs but 1.4% more accuracy. The source code will be provided in supplementary materials. CCS Concepts: center dot Computing methodologies -> Visual content-based indexing and retrieval;
关键词	Food Recognition Lightweight Aggregation Block FLOPs
DOI	10.1145/3680285
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001364226700002
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41149
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sheng, Guorui
作者单位	1.Ludong Univ, Sch Informat & Elect Engn, Yantai, Peoples R China 2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yang, Yancun,Min, Weiqing,Song, Jingru,et al. Lightweight Food Recognition via Aggregation Block and Feature Encoding[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2024,20(10):25.
APA	Yang, Yancun,Min, Weiqing,Song, Jingru,Sheng, Guorui,Wang, Lili,&Jiang, Shuqiang.(2024).Lightweight Food Recognition via Aggregation Block and Feature Encoding.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,20(10),25.
MLA	Yang, Yancun,et al."Lightweight Food Recognition via Aggregation Block and Feature Encoding".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 20.10(2024):25.