Places: A 10 Million Image Database for Scene Recognition
Open accessAbstract
The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches. Visualization of the CNNs trained on Places shows that object detectors emerge as an intermediate representation of scene classification. With its high-coverage and high-diversity of exemplars, the Places Database along with the Places-CNNs offer a novel resource to guide future progress on scene recognition problems.
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The Places Database is a collection of 10 million labeled scene photographs from the environment types encountered in the world.
“The Places Database [41] is a collection of 10 million labeled scene photographs from the environment types encountered in the world.”
From EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge (2023) · cites this paper as [41]
Places-CNNs can be used for scene category and attribute identification.
“It also provides pre-trained scene classification CNNs (Places-CNNs) which can be used for scene category and attribute identification.”
From EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge (2023) · cites this paper as [41]
The Places Database has a collection of 10 million labeled scene photographs from around the world and offers various pretrained CNNs for scene classification.
“The Places Database [29] has a massive collection with 10 million labeled scene photographs from around the world. Additionally, it offers various pretrained CNNs (Places-CNNs) for scene classification, which can be used for scene category and attribute identification.”
From SAFER: Situation Aware Facial Emotion Recognition (2023) · cites this paper as [29]
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“The Places Database [29] has a massive collection with 10 million labeled scene photographs from around the world.”
BackgroundEMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge
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“The Places Database [41] is a collection of 10 million labeled scene photographs from the environment types encountered in the world.”
BackgroundMethod
- Authors
- Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2018-06-01
- Volume
- 40
- Issue
- 6
- Pages
- 1452-1464
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2732026016
- Cited by
- 4,042
- References
- 11
- Citations by year
- 2026: 284 · 2025: 484 · 2024: 524 · 2023: 572 · 2022: 574 · 2021: 586 · 2020: 473 · 2019: 347 · 2018: 177 · 2017: 21
- FWCI
- 114.08
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- US National Science Foundation (1016862 to A.O., 1524817 to A.T.)
- Basic Research Office of the Assistant Secretary of Defense for Research and Engineering
- Office of Naval Research (N00014-16-1-3116 to A.O.)
- MIT Big Data Initiative at CSAIL
- Toyota Research Institute / MIT CSAIL Joint Research Center, Google, Xerox and Amazon Awards
- NVIDIA Corporation
- Facebook Fellowship
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- scene classification, large-scale image databases, convolutional neural networks, object detectors, image classification benchmark
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