Beyond local image features: Scene calssification using supervised semantic representation
文献类型：会议论文
| 作者 | Chunjie Zhang ; Jing Liu; Chao Liang; Jinhui Tang; Hanqing Lu
|
| 出版日期 | 2012 |
| 会议日期 | September 30 - October 3, 2012 |
| 会议地点 | Lake Buena Vista, Orlando, FL, USA |
| 关键词 | Semantic Representation Scene Classification Sparse Supervised Learning |
| 英文摘要 | The use of local features for image representation has been proven very effective for a variety of visual tasks such as object localization and scene classification. However, local image features carry little semantic information which is potentially not enough for high level visual tasks. To solve this problem, in this paper, we propose to use a supervised semantic image representation for scene classification, where an image is represented as a response histogram. This response histogram is a combination of the prediction of pre-trained generic object classifiers and classifiers generated by supervised learning. Besides, the use of sparsity constraints makes the proposed representation more efficient and effective to compute. Performances on the UIUC-Sports dataset, the MIT Indoor scene dataset and the Scene-15 dataset demonstrate the effectiveness of the proposed method. |
| 会议录 | 无
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13448]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 通讯作者 | Jing Liu |
| 推荐引用方式 GB/T 7714 | Chunjie Zhang,Jing Liu,Chao Liang,et al. Beyond local image features: Scene calssification using supervised semantic representation[C]. 见:. Lake Buena Vista, Orlando, FL, USA. September 30 - October 3, 2012. |
入库方式： OAI收割
来源：自动化研究所
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