Abstract
Local features of images have been widely used in image retrieval, however, the cost is so heavy. To address this issue, a superpixel-based approach for image retrieval is proposed. We first extract the image structure that preserves the main information and removes the redundant information from the image by smoothing and oversegment a smoothed image into a certain number of superpixels. We then extract the positive candidate superpixels by combining superpixels with local descriptors. Finally, we compute the similarity of two images by analyzing two sets of positive candidate superpixels. Experiments on dataset PQ7 demonstrate the performance of the proposed approach.
| Original language | English |
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| Title of host publication | ICVIP 2017 : Proceedings of the International Conference on Video and Image Processing |
| Publisher | Association for Computing Machinery |
| Pages | 156-160 |
| ISBN (Print) | 9781450353830 |
| DOIs | |
| Publication status | Published - 27 Dec 2017 |
| Event | 2017 International Conference on Video and Image Processing (ICVIP 2017) - Nanyang Executive Centre, Singapore, Singapore Duration: 27 Dec 2017 → 29 Dec 2017 http://www.icvip.org/Full_Schedule.pdf |
Conference
| Conference | 2017 International Conference on Video and Image Processing (ICVIP 2017) |
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| Abbreviated title | ICVIP 2017 |
| Place | Singapore |
| City | Singapore |
| Period | 27/12/17 → 29/12/17 |
| Internet address |
Bibliographical note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).Research Keywords
- Image retrieval
- Key-point
- Smoothing
- Superpixel