Big visual data analysis : scene classification and geometric labeling /
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| Author / Creator: | Chen, Chen (Computer vision scientist) |
|---|---|
| Imprint: | Singapore : Springer, 2016. |
| Description: | 1 online resource (x, 122 pages) : illustrations (color illustrations) |
| Language: | English |
| Series: | SpringerBriefs in electrical and computer engineering, Signal processing, 2191-8112 SpringerBriefs in electrical and computer engineering. Signal processing. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11253201 |
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| 100 | 1 | |a Chen, Chen |c (Computer vision scientist) |0 http://id.loc.gov/authorities/names/no2020080873 | |
| 245 | 1 | 0 | |a Big visual data analysis : |b scene classification and geometric labeling / |c Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo. |
| 264 | 1 | |a Singapore : |b Springer, |c 2016. | |
| 300 | |a 1 online resource (x, 122 pages) : |b illustrations (color illustrations) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 347 | |b PDF | ||
| 490 | 1 | |a SpringerBriefs in electrical and computer engineering, Signal processing, |x 2191-8112 | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Introduction -- Scene Understanding Datasets -- Indoor/Outdoor classification with Multiple Experts -- Outdoor Scene Classification Using Labeled Segments -- Global-Attributes Assisted Outdoor Scene Geometric Labeling -- Conclusion and Future Work. | |
| 520 | |a This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks. | ||
| 546 | |a English. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed March 1, 2016). | |
| 650 | 0 | |a Computer vision. |0 http://id.loc.gov/authorities/subjects/sh85029549 | |
| 650 | 0 | |a Image processing |x Digital techniques. |0 http://id.loc.gov/authorities/subjects/sh85064447 | |
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
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| 650 | 7 | |a Computer vision. |2 fast |0 (OCoLC)fst00872687 | |
| 650 | 7 | |a Image processing |x Digital techniques. |2 fast |0 (OCoLC)fst00967508 | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Ren, Yuzhuo, |e author. |0 http://id.loc.gov/authorities/names/nb2016022862 | |
| 700 | 1 | |a Kuo, C.-C. Jay |q (Chung-Chieh Jay), |e author. |0 http://id.loc.gov/authorities/names/n89673531 | |
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| 830 | 0 | |a SpringerBriefs in electrical and computer engineering. |p Signal processing. | |
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