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| Presentation | 2012-09-03 10:30 Nonparametric Bayesian Estimation for Automatic Image Annotation Using Gaussian Mixture Model Yukihiro Tsuboshita, Noriji Kato (Fuji Xerox), Masato Okada (The universisty of Tokyo)  PRMU2012-40 IBISML2012-23 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Automatic image annotation (AIA) is a process to automatically assign metadata to a digital image in the form of captions or keywords. Here we reported the effort of the improvement with respect to a learning based AIA algorithm using Gaussian mixture model (GMM) as a probabilistic model. In Supervised Multiclass Labeling (SML), which is a conventional method to use GMM, the number of mixed components is identical to all labels. The performance of GMM is known to fully depend on the number of mixed component. Therefore, in the present study, the number of components not being determined in advance, we tried to make the GMM to learn the optimal number of components from given training data. More precisely, we introduced the GMM to Dirichlet process, which is commonly used in the nonparametric Bayesian estimation, as a generating process of mixed components. As the result of evaluation tests using Corel 5K database, which is a standard test collection for image annotation, we found the proposed method exhibited more stable performance than the standard SML. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Automatic image annotation / Machine learning / Gaussian mixture model / Nonparametric Bayesian model / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 197, PRMU2012-40, pp. 93-98, Sept. 2012. | |
| Paper # | PRMU2012-40 | |
| Date of Issue | 2012-08-26 (PRMU, IBISML) | |
| ISSN | Print edition: ISSN 0913-5685 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 | PRMU2012-40 IBISML2012-23 |
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| Conference Information | |
| Committee | PRMU IBISML IPSJ-CVIM |
| Conference Date | 2012-09-02 - 2012-09-03 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2012-09-PRMU-IBISML-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Nonparametric Bayesian Estimation for Automatic Image Annotation Using Gaussian Mixture Model |
| Sub Title (in English) | |
| Keyword(1) | Automatic image annotation |
| Keyword(2) | Machine learning |
| Keyword(3) | Gaussian mixture model |
| Keyword(4) | Nonparametric Bayesian model |
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| 1st Author's Name | Yukihiro Tsuboshita |
| 1st Author's Affiliation | Fuji Xerox co. ltd. (Fuji Xerox) |
| 2nd Author's Name | Noriji Kato |
| 2nd Author's Affiliation | Fuji Xerox co. ltd. (Fuji Xerox) |
| 3rd Author's Name | Masato Okada |
| 3rd Author's Affiliation | The university of Tokyo (The universisty of Tokyo) |
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| Speaker | Author-1 |
| Date Time | 2012-09-03 10:30:00 |
| Presentation Time | 30 minutes |
| Registration for | PRMU |
| Paper # | PRMU2012-40, IBISML2012-23 |
| Volume (vol) | vol.112 |
| Number (no) | no.197(PRMU), no.198(IBISML) |
| Page | pp.93-98 |
| #Pages | 6 |
| Date of Issue | 2012-08-26 (PRMU, IBISML) |
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