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| Presentation | 2021-12-16 15:10 Neural Network-based Local Feature Descriptors for Matching Excavated Mokkan Fragments of Various Sizes Trung Tan Ngo, Hung Tuan Nguyen, Masaki Nakagawa (TUAT)  PRMU2021-33 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper presents a method to predict whether two excavated Mokkan fragments of various sizes are from the same tablet or not using an end-to-end attention-based neural network, namely A-VLAD. The method does not require any preprocessing stages such as binarization and segmentation. It has three main parts: a local feature extractor using Convolutional Neural Network from an input image, an attention filter for key-points selection, and a generalized deep neural network-based VLAD model to aggregate the extracted key-points and form a representative vector. The whole network is trained end-to-end using the stochastic gradient descent algorithm to optimize both cross-entropy and triplet losses. In the experiments, we evaluate the proposed model on 13,205 fragments broken from 556 complete wooden tablets excavated from the Heijo-Kyo Palace ruins in the Japanese Nara period. The proposed A-VLAD model achieved mean average precision of 75.5% and top-1 accuracy of 87.9% better than the state-of-the-art methods on this Mokkan dataset. Thus, it is expected to be used to support archaeologists to assemble Mokkan fragments to recover an original Mokkan tablet. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Mokkan / Historical documents / Image retrieval / Convolution neural network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-33, pp. 51-56, Dec. 2021. | |
| Paper # | PRMU2021-33 | |
| Date of Issue | 2021-12-09 (PRMU) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PRMU2021-33 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2021-12-16 - 2021-12-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-12-PRMU |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Neural Network-based Local Feature Descriptors for Matching Excavated Mokkan Fragments of Various Sizes |
| Sub Title (in English) | |
| Keyword(1) | Mokkan |
| Keyword(2) | Historical documents |
| Keyword(3) | Image retrieval |
| Keyword(4) | Convolution neural network |
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| 1st Author's Name | Trung Tan Ngo |
| 1st Author's Affiliation | Tokyo University of Agriculture and Technology (TUAT) |
| 2nd Author's Name | Hung Tuan Nguyen |
| 2nd Author's Affiliation | Tokyo University of Agriculture and Technology (TUAT) |
| 3rd Author's Name | Masaki Nakagawa |
| 3rd Author's Affiliation | Tokyo University of Agriculture and Technology (TUAT) |
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| Speaker | Author-1 |
| Date Time | 2021-12-16 15:10:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2021-33 |
| Volume (vol) | vol.121 |
| Number (no) | no.304 |
| Page | pp.51-56 |
| #Pages | 6 |
| Date of Issue | 2021-12-09 (PRMU) |
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