ANALYSIS OF FOOD IMAGES: FEATURES AND CLASSIFICATION

被引：0

作者：

He, Ye ^{[1
]}

Xu, Chang ^{[1
]}

Khanna, Nitin ^{[2
]}

Boushey, Carol J. ^{[3
]}

Delp, Edward J. ^{[1
]}

机构：

[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

[2] Graph Era Univ, Dept Elect & Commun Engn, Dehra Dun, Uttar Pradesh, India

[3] Univ Hawaii, Ctr Canc, Canc Epidemiol Program, Honolulu, HI 96822 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年

关键词：

Dietary Assessment; Image Classification; Food Identification; Vocabulary Trees;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.

引用

页码：2744 / 2748

页数：5

共 16 条

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