CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Beyond local image features: Scene calssification using supervised semantic representation
Chunjie Zhang; Jing Liu; Chao Liang; Jinhui Tang; Hanqing Lu
2012
Conference NameIEEE International Conference on Image Processing
Source Publication
Conference DateSeptember 30 - October 3, 2012
Conference PlaceLake Buena Vista, Orlando, FL, USA
AbstractThe 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.
KeywordSemantic Representation Scene Classification Sparse Supervised Learning
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13448
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorJing Liu
Recommended Citation
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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