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Improving Large-Scale Image Retrieval using Geometric Weighting
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sezganov, Dmitry Porat, Moshe |
| Abstract | central in large-scale image retrieval. The geometrical information is usually involved only in the post-processing spatial verification step usually implemented with the RANdom SAmple Consensus (RANSAC) algorithm. To enable visual search in real-time, RANSAC can be applied only to a relatively small number of top candidates due to its computational requirements. In this work, we propose an alternative method to perform accurate spatial verification with a significantly lower computational cost. Experimental results show that the proposed method outperforms the baseline BOF, and achieves similar performance as RANSAC based spatial verification, despite the major difference in complexity. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://psrcentre.org/images/extraimages/7%20812057.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
