Knowledge Commons of Institute of Automation,CAS
Beyond local image features: Scene calssification using supervised semantic representation | |
Chunjie Zhang; Jing Liu; Chao Liang; Jinhui Tang; Hanqing Lu | |
2012 | |
会议名称 | IEEE International Conference on Image Processing |
会议录名称 | 无 |
会议日期 | September 30 - October 3, 2012 |
会议地点 | Lake Buena Vista, Orlando, FL, USA |
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. |
关键词 | Semantic Representation Scene Classification Sparse Supervised Learning |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/13448 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
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],2012. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
icip 2012.pdf(195KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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