Discovering Class-Specific Spatial Layouts for Scene Recognition
Scene image is a spatial composition of objects and background contexts and finding discriminative spatial layouts is critical for scene recognition. In this letter, we propose an ℓ1-regularized max-margin formulation to discover class-specific spatial layouts by jointly learning the image classifie...
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| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2017
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/82238 http://hdl.handle.net/10220/43502 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Scene image is a spatial composition of objects and background contexts and finding discriminative spatial layouts is critical for scene recognition. In this letter, we propose an ℓ1-regularized max-margin formulation to discover class-specific spatial layouts by jointly learning the image classifier and the class-specific spatial layouts for scene recognition. Unlike previous methods that classify images into different categories either without considering the spatial layouts explicitly or only using class generic spatial layout, our proposed method can discover a sparse combination of class-specific spatial layouts for different scenes and boost the recognition performance. Experiments on scene-15, landuse-21, and MIT indoor-67 datasets validate the advantages of our proposed algorithm. |
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