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|a 10.1109/TPAMI.2017.2723009
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhou, Bolei
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Places
|b A 10 Million Image Database for Scene Recognition
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|c 2018
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|a Text
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|a Date Completed 04.04.2019
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|a Date Revised 04.04.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Lapedriza, Agata
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Khosla, Aditya
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Oliva, Aude
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Torralba, Antonio
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 40(2018), 6 vom: 01. Juni, Seite 1452-1464
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:40
|g year:2018
|g number:6
|g day:01
|g month:06
|g pages:1452-1464
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2017.2723009
|3 Volltext
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