| Presentation | 2013-01-23 Keypoint Selection based on Diverse Density for Image Retrieval Keita YUASA, Toshikazu WADA, Hiroshi OIKE, Jun SAKATA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We are planning to construct an image retrieval system using FPGA. For designing this system, we developed a prototype system, which retrieves the image having maximum number of matched local image features with a query image. The reason why we don't use Bag of Features (BoF) is that codebook referencing may consume considerable time and computational resources on FPGA. For this purpose, the local features describing a stored image should satisfy the following conditions : 1) they should have strong discrimination power from other images, 2) they should be robust agamst observation distortions including rotation, scaling, and so on. In order to maximize the number of stored images, the number of local features describing a stored image should be minimized For selecting such "good local features" from all local features, we propose a method based on Diverse Density. In the experiment, we confine the number of local features describing a single image to 10, and our method outperforms other local feature selection methods. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | imageretrieval / local feature selection / Diverse Density / discrimination power / robustness against distortions |
| Paper # | PRMU2012-91,MVE2012-56 |
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| Conference Information | |
| Committee | MVE |
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| Conference Date | 2013/1/16(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Media Experience and Virtual Environment (MVE) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Keypoint Selection based on Diverse Density for Image Retrieval |
| Sub Title (in English) | |
| Keyword(1) | imageretrieval |
| Keyword(2) | local feature selection |
| Keyword(3) | Diverse Density |
| Keyword(4) | discrimination power |
| Keyword(5) | robustness against distortions |
| 1st Author's Name | Keita YUASA |
| 1st Author's Affiliation | Faculty of Engineering, Wakayama University() |
| 2nd Author's Name | Toshikazu WADA |
| 2nd Author's Affiliation | Faculty of Engineering, Wakayama University |
| 3rd Author's Name | Hiroshi OIKE |
| 3rd Author's Affiliation | Faculty of Engineering, Wakayama University |
| 4th Author's Name | Jun SAKATA |
| 4th Author's Affiliation | Faculty of Engineering, Wakayama University |
| Date | 2013-01-23 |
| Paper # | PRMU2012-91,MVE2012-56 |
| Volume (vol) | vol.112 |
| Number (no) | 386 |
| Page | pp.pp.- |
| #Pages | 6 |
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