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
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
专题模式识别国家重点实验室_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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