Memory Reduction and Stability Improvement for Real-Time Document Image Retrieval with a Database of 10 Million Pages
| Language | Japanese |
|---|---|
| Type | Proceedings of Japanese Conferences |
| Authors | Kazutaka Takeda, Koichi Kise, Masakazu Iwamura |
| Title | Memory Reduction and Stability Improvement for Real-Time Document Image Retrieval with a Database of 10 Million Pages |
| Journal | IEICE Technical Report |
| Vol. | 110 |
| No. | 467 |
| Presentation number | PRMU2010-275 |
| Location | 茨城県つくば市 |
| Reviewed | Not reviewed |
| Date | March 2011 |
| Research fields | Document Image Analysis, Document Image Retrieval |
| Key | Takeda2011_IEICEPRMU (id=1197) |
Abstract
In this paper, we propose a large-scale document image retrieval method which realizes real-time processing with Locally Likely Arrangement Hashing (LLAH). Although LLAH has high accuracy and robustness, it requires a large amount of memory. It is also required to increase the discrimination power and stability of features for scaling up the database. For these purposes, we introduce the following three improvements. The first one is reduction of the required amount of memory by sampling feature points stored in the database. The second improvement is to increase the discrimination power by increasing the dimension of features. The last one is advancement of stability by removing redundant dimensions of features. From the experimental results, we have confirmed that the proposed improvements help to realize accuracy of 99.4% and processing time of 38ms for the database of 10 million pages.
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