MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection

被引：26

作者：

Bashiri, Fereshteh S. ^{[1
,2
]}

LaRose, Eric ^{[2
]}

Peissig, Peggy ^{[2
]}

Tafti, Ahmad P. ^{[2
]}

机构：

[1] Univ Wisconsin, Dept Elect Engn, Madison, WI 53706 USA

[2] Marshfield Clin Res Inst, Biomed Informat Res Ctr, Marshfield, WI 54449 USA

来源：

DATA IN BRIEF | 2018年 / 17卷

关键词：

Image dataset; Large-scale dataset; Image classification; Supervised learning; Indoor objects; Deep learning;

D O I：

10.1016/j.dib.2017.12.047

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

A fully-labeled image dataset provides a unique resource for reproducible research inquiries and data analyses in several computational fields, such as computer vision, machine learning and deep learning machine intelligence. With the present contribution, a large-scale fully-labeled image dataset is provided, and made publicly and freely available to the research community. The current dataset entitled MCIndoor20000 includes more than 20,000 digital images from three different indoor object categories, including doors, stairs, and hospital signs. To make a comprehensive dataset addressing current challenges that exist in indoor objects modeling, we cover a multiple set of variations in images, such as rotation, intra-class variation plus various noise models. (C) 2018 The Authors. Published by Elsevier Inc.

引用

页码：71 / 75

页数：5

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