Large-Scale Image Retrieval by Hashing of Local Features
| Language | Japanese |
|---|---|
| Type | Journal Articles |
| Authors | Koichi Kise, Masakazu Iwamura, Tomohiro Nakai, Kazuto Noguchi |
| Title | Large-Scale Image Retrieval by Hashing of Local Features |
| Journal | DBSJ Journal |
| Vol. | 8 |
| No. | 1 |
| Pages | pp.119-124 |
| Reviewed | Reviewed |
| Date | June 2009 |
| Research fields | Document Image Analysis, Document Image Retrieval, Object Recognition |
| URL | http://dbsj.org/journal/dbsj_journal/dbsj_journal_vol_8_no_1_119_124/ |
| Key | Kise2009_DBSJ (id=1088) |
Abstract
Image retrieval with query images is a task to find their corresponding images (near duplicates) from the database. It is an important task since it has many applications such as a replacement of barcodes and copyright protection of images. The task of retrieval is not trivial since query images are often far different from their corresponding images in the database due to different imaging conditions. Although this problem can be solved using local features, which are known to be robust to change of the imaging conditions, they pose a problem of processing time since their number is huge. In this paper, we describe our new retrieval methods based on hashing for solving the above problem. As images in the database, we deal with ordinary images of scenes, objects and people, etc., as well as document images that are completely dissimilar to ordinary images. Local features for each type and the common notion for successful retrieval are shown.
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