Abstract
Detection and description of local features are a classical problem in image processing and multimedia content analysis. Based on the in homogeneity of visual orientation in human visual system, we propose a novel algorithm S-SIFT to detect and describe local image features. In three stages of S-SIFT, the information from the least discriminability orientation is omitting. Compared with the standard SIFT algorithm, S-SIFT has lower dimension and provides a faster key point matching. Experiments on the standard dataset demonstrate that our algorithm yields comparable or even better results for feature detection and matching tasks.
Original language | English |
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Title of host publication | Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 |
Pages | 669-672 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2012 |
Event | 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China Duration: 4 Dec 2012 → 7 Dec 2012 |
Conference
Conference | 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 |
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Country/Territory | China |
City | Macau |
Period | 4/12/12 → 7/12/12 |
Keywords
- descriptors
- real-world distribution
- scale-invariant feature transform
- visual orientation
ASJC Scopus subject areas
- Artificial Intelligence
- Software