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MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection
Fereshteh S Bashiri1,2, Eric LaRose2, Peggy Peissig2
1Department of Electrical Engineering, University of Wisconsin-Milwaukee, WI, USA.
Data in Brief
|June 8, 2018
Summary
A new, large-scale, fully-labeled image dataset, MCIndoor20000, is now available for computer vision and machine learning research. It features over 20,000 images of indoor objects with variations to aid reproducible research.
Area of Science:
- Computer Vision
- Machine Learning
- Deep Learning
Background:
- Fully-labeled image datasets are crucial for reproducible research in computational fields.
- Existing datasets may lack diversity or comprehensive variations for indoor object modeling.
Purpose of the Study:
- To introduce and release a large-scale, fully-labeled image dataset for indoor object recognition.
- To provide a resource that addresses current challenges in indoor object modeling.
Main Methods:
- Collected and curated over 20,000 digital images.
- Focused on three indoor object categories: doors, stairs, and hospital signs.
- Incorporated image variations including rotation, intra-class variation, and noise models.
Main Results:
- A comprehensive dataset named MCIndoor20000 was created.
- The dataset includes diverse indoor object images with controlled variations.
- The dataset is made publicly and freely available.
Conclusions:
- The MCIndoor20000 dataset offers a valuable resource for advancing research in computer vision and machine learning.
- Its public availability promotes reproducible research and development in indoor scene understanding.
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