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.
Places: A 10 Million Image Database for Scene Recognition
Bolei Zhou,Àgata Lapedriza,A. Khosla,A. Oliva,A. Torralba
Published 2018 in IEEE Transactions on Pattern Analysis and Machine Intelligence
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- Publication year
2018
- Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
- Publication date
2018-06-01
- Fields of study
Medicine, Computer Science, Engineering, Environmental Science
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Semantic Scholar, PubMed
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