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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yan-Tao Zheng Ming Zhao Shi-Yong Neo Tat-Seng Chua Qi Tian |
| Copyright Year | 2008 |
| Description | Author affiliation: Nat. Univ. of Singapore, Singapore (Yan-Tao Zheng) |
| Abstract | We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic dasiasemanticspsila, i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. The tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 527318 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424422425 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2008.4587611 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Probability distribution Object recognition Bridges Vector quantization Robustness Motorcycles Bicycles Testing Image representation Solid modeling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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