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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li Tan Yuanda Cao Minghua Yang Qiaoyan He |
| Copyright Year | 2008 |
| Description | Author affiliation: Sch. of Comput. Sci. & Technol., Beijing Inst. of Technol., Beijing (Li Tan; Yuanda Cao; Minghua Yang; Qiaoyan He) |
| Abstract | Recent work in visual retrieval shows that bag-of-features (BoF) has appeared promising for object recognition and categorization. Local descriptors such as SIFT have shown impressive results on objects. The main idea of BoF is to depict each image as an orderless collection of local keypoint features. However, not all the local keypoint features are important for retrieving objects and rather, the user is often interested in saliency regions of object classes. Therefore, we propose a new method for modeling attention objects with local descriptors. The proposed model in conjunction with a biologically motivated selective attention model can extract the salient regions of each image. In order to model attention objects, we propose a new attention-based SIFT algorithm using scale contract information and local keypoint features to reflect more exact saliency in object classes. Computer experimental results on both Caltech 101 object category datasets and TRECVID2007 datasets shows that the proposed model can generate competitive performance. |
| Starting Page | 75 |
| Ending Page | 79 |
| File Size | 553521 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769533049 |
| DOI | 10.1109/ICNC.2008.649 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Vocabulary Biological system modeling Computational modeling Clustering algorithms Detectors Biology computing Object recognition Helium Data mining |
| Content Type | Text |
| Resource Type | Article |
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