EFFICIENT SUPERPIXEL BASED SEGMENTATION FOR FOOD IMAGE ANALYSIS

被引：0

作者：

Wang, Yu ^{[1
]}

Liu, Chang ^{[1
]}

Zhu, Fengqing ^{[1
]}

Boushey, Carol J. ^{[2
]}

Delp, Edward J. ^{[1
]}

机构：

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

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

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2016年

关键词：

image segmentation; superpixel; graph model; nutrient analysis; TEXTURE CLASSIFICATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a segmentation method based on normalized cut and superpixels. The method relies on color and texture cues for fast computation and efficient use of memory. The method is used for food image segmentation as part of a mobile food record system we have developed for dietary assessment and management. The accurate estimate of nutrients relies on correctly labelled food items and sufficiently well-segmented regions. Our method achieves competitive results using the Berkeley Segmentation Dataset and outperforms some of the most popular techniques in a food image dataset.

引用

页码：2544 / 2548

页数：5

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