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A supervised LPP and Neural Network based Scene classification with Color Histogram and Camera Metadata
https://ipsj.ixsq.nii.ac.jp/records/61527
https://ipsj.ixsq.nii.ac.jp/records/61527b1d479e9-73c4-4431-9aa5-c2333b040906
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-CVIM09166039 (3.6 MB)
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Copyright (c) 2009 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | SIG Technical Reports(1) | |||||||
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| 公開日 | 2009-03-06 | |||||||
| タイトル | ||||||||
| タイトル | A supervised LPP and Neural Network based Scene classification with Color Histogram and Camera Metadata | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | A supervised LPP and Neural Network based Scene classification with Color Histogram and Camera Metadata | |||||||
| 言語 | ||||||||
| 言語 | jpn | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||
| 資源タイプ | technical report | |||||||
| 著者所属 | ||||||||
| College of Information Science and Engineering, Ritsumeikan University | ||||||||
| 著者所属 | ||||||||
| College of Information Science and Engineering, Ritsumeikan University | ||||||||
| 著者所属 | ||||||||
| Digital Technology Research Center, SANYO Electric Co., Ltd. | ||||||||
| 著者所属 | ||||||||
| Digital Technology Research Center, SANYO Electric Co., Ltd. | ||||||||
| 著者所属 | ||||||||
| Digital Technology Research Center, SANYO Electric Co., Ltd. | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| College of Information Science and Engineering, Ritsumeikan University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| College of Information Science and Engineering, Ritsumeikan University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Digital Technology Research Center, SANYO Electric Co., Ltd. | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Digital Technology Research Center, SANYO Electric Co., Ltd. | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Digital Technology Research Center, SANYO Electric Co., Ltd. | ||||||||
| 著者名 |
Xian-HuaHan
× Xian-HuaHan
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| 著者名(英) |
Xian-Hua, Han
× Xian-Hua, Han
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| 論文抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Scene classification (e.g., landscape, sunset, night-landscape, etc.) is still a challenging problem in computer vision. Scene classification based only on low-level vision cues has had limited success on unconstrained image sets. In other hand, camera metadata related to capture conditions provides cues independent of the captured scene content that can be used to improve classification performance. Analysis of camera metadata statistics for images of each class revealed that some metadata fields are most discriminative for some classes. So, in this paper, we proposed to use the combined feature of scene color histogram and camera metadata, and then using supervised Locality preserving projection (LPP) for feature space transformation and dimension reduction, and finally, adapt Probabilistic neural network for scene classification. Experimental results show that the classification accuracy rate can be improved compared with using PCA (Principal Component Analysis) subspace learning method, and are also better than that with only the low-level vision feature (color histogram). | |||||||
| 論文抄録(英) | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Scene classification (e.g., landscape, sunset, night-landscape, etc.) is still a challenging problem in computer vision. Scene classification based only on low-level vision cues has had limited success on unconstrained image sets. In other hand, camera metadata related to capture conditions provides cues independent of the captured scene content that can be used to improve classification performance. Analysis of camera metadata statistics for images of each class revealed that some metadata fields are most discriminative for some classes. So, in this paper, we proposed to use the combined feature of scene color histogram and camera metadata, and then using supervised Locality preserving projection (LPP) for feature space transformation and dimension reduction, and finally, adapt Probabilistic neural network for scene classification. Experimental results show that the classification accuracy rate can be improved compared with using PCA (Principal Component Analysis) subspace learning method, and are also better than that with only the low-level vision feature (color histogram). | |||||||
| 書誌レコードID | ||||||||
| 収録物識別子タイプ | NCID | |||||||
| 収録物識別子 | AA11131797 | |||||||
| 書誌情報 |
研究報告コンピュータビジョンとイメージメディア（CVIM） 巻 2009, 号 29(2009-CVIM-166), p. 257-262, 発行日 2009-03-06 |
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| Notice | ||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||
| 出版者 | ||||||||
| 言語 | ja | |||||||
| 出版者 | 情報処理学会 | |||||||
