Food Places Classification in Egocentric Images Using Siamese Neural Networks

被引：0

作者：

Kamal Sarker, Md Mostafa ^{[1
]}

Furruka Banu, Syeda ^{[2
]}

Rashwan, Hatem A. ^{[1
]}

Abdel-Nasser, Mohamed ^{[1
]}

Kumar Singh, Vivek ^{[1
]}

Chambon, Sylvie ^{[3
]}

Radeva, Petia ^{[4
]}

Puig, Domenec ^{[1
]}

机构：

[1] Rovira & Virgili Univ, DEIM, Tarragona 43007, Spain

[2] Rovira & Virgili Univ, ETSEQ, Tarragona 43007, Spain

[3] Univ Toulouse, INP ENSEEIHT, CNRS IRIT, F-31071 Toulouse, France

[4] Univ Barcelona, Dept Math, Barcelona 08007, Spain

来源：

ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT | 2019年 / 319卷

关键词：

Egocentric vision; food pattern classification; siamese neural networks; one-shot learning; scene classification;

D O I：

10.3233/FAIA190117

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with 'MiniEgoFoodPlaces' with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the "MiniEgoFoodPlaces" dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.

引用

页码：145 / 151

页数：7

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