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| Presentation | 2022-12-15 10:45 DN4C -- An Interactive Image Segmentation System Combining Deep Neural Network and Nearest Neighbor Classifier -- Toshikazu Wada, Koji Kamma (Wakayama University)  PRMU2022-35 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Color/texture based image segmentation can be widely applied to the images for product and/or medical inspection, remote-sensing, and so on. The accuracy and the robustness against the image noise are drastically improved recently by the introduction of Deep Neural Networks: DNNs. However, depending on the products, lesions, ground surface or vegetation to be examined, training images are quite different, and hence, people cannot enjoy the accurate segmentation system without training the system by themselves. Most of the previous training framework is batch style: providing huge amount of image and annotation pairs and training to minimize the loss function defined by these pairs. On the other hand, if we provide incomplete annotations, like scribbles on small number of images, the system still can learn incomplete segmentation rules. By adding new annotations, the system can learn better rules. By iterating annotation, learning, and segmentation, we can realize “interactive segmentation”, which reduces the annotation tasks. This report proposes a system for interactive segmentation system DN4C by combining DNN and Nearest Neighbor Classifier: NNC. In the pure DNN image segmentation system, the feature distributions at the layer before the output must be linearly separable for producing compatible result with annotations. DN4C have no such limitations. It implies shallower network layers are sufficient, and hence, the faster learning is possible. In the experiment, we examined interactive segmentation is possible using the prototype system. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Image Segmentation / Deep Neural Network / Nearest Neighbor Classifier / Human In the Loop / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 314, PRMU2022-35, pp. 19-24, Dec. 2022. | |
| Paper # | PRMU2022-35 | |
| Date of Issue | 2022-12-08 (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 | PRMU2022-35 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2022-12-15 - 2022-12-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Toyama International Conference Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2022-12-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | DN4C |
| Sub Title (in English) | An Interactive Image Segmentation System Combining Deep Neural Network and Nearest Neighbor Classifier |
| Keyword(1) | Image Segmentation |
| Keyword(2) | Deep Neural Network |
| Keyword(3) | Nearest Neighbor Classifier |
| Keyword(4) | Human In the Loop |
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| 1st Author's Name | Toshikazu Wada |
| 1st Author's Affiliation | Wakayama University (Wakayama University) |
| 2nd Author's Name | Koji Kamma |
| 2nd Author's Affiliation | Wakayama University (Wakayama University) |
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| Speaker | Author-1 |
| Date Time | 2022-12-15 10:45:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2022-35 |
| Volume (vol) | vol.122 |
| Number (no) | no.314 |
| Page | pp.19-24 |
| #Pages | 6 |
| Date of Issue | 2022-12-08 (PRMU) |
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