PFID: PITTSBURGH FAST-FOOD IMAGE DATASET

被引：145

作者：

Chen, Mei ^{[1
]}

Dhingra, Kapil ^{[3
]}

Wu, Wen ^{[2
]}

Yang, Lei ^{[2
]}

Sukthankar, Rahul ^{[1
]}

Yang, Jie ^{[2
]}

机构：

[1] Intel Labs Pittsburgh, Pittsburgh, PA USA

[2] Carnegie Mellon Univ, Pittsburgh, PA USA

[3] Columbia Univ, New York, NY 10027 USA

来源：

2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6 | 2009年

关键词：

Food image dataset; object recognition;

D O I：

10.1109/ICIP.2009.5413511

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360 degrees videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.

引用

页码：289 / +

页数：3

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