| Paper Abstract and Keywords |
| Presentation |
2021-02-18 16:40
A note on improvement of image sentiment analysis based on introduction of image captioning
Yun Liang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Recently, with the popularization of social network services, the images uploaded by users have been increasing. Users tend to share their feelings by sharing pictures. Thus, it is important to analyze image sentiments for effectively collecting and searching from a large amount of data. Previous advances on image sentiment analysis paid attention to the features of the images from low levels such as shape, color and objects, to high levels such as aesthetics and semantics. Furthermore, with the usage of deep metric learning, researchers considered the spatial distribution of the sentiments. However, the aforementioned features cannot reveal the details such as the correlations among objects of a certain image. Therefore, in this paper, to achieve a performance improvement for the image sentiment analysis, we newly introduce image captioning. Concretely, we extract the multi-level features of the images by our convolutional network. Furthermore, we extract the image captioning features by an employed pre-trained image captioning model. Moreover, we integrate the aforementioned features and predict the image sentiments by using the integrated features. We have confirmed the effectiveness of the proposed method through experiments using an open dataset. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Deep metric learning / Image sentiment analysis / Image captioning / / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 45, no. 4, ME2021-13, pp. 65-69, Feb. 2021. |
| Paper # |
ME2021-13 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
IEICE-IE IEICE-ITS MMS ME AIT |
| Conference Date |
2021-02-18 - 2021-02-19 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Online |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image Processing, etc. |
| Paper Information |
| Registration To |
ME |
| Conference Code |
2021-02-IE-ITS-MMS-ME-AIT |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
A note on improvement of image sentiment analysis based on introduction of image captioning |
| Sub Title (in English) |
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| Keyword(1) |
Deep metric learning |
| Keyword(2) |
Image sentiment analysis |
| Keyword(3) |
Image captioning |
| Keyword(4) |
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| 1st Author's Name |
Yun Liang |
| 1st Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 2nd Author's Name |
Keisuke Maeda |
| 2nd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 3rd Author's Name |
Takahiro Ogawa |
| 3rd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 4th Author's Name |
Miki Haseyama |
| 4th Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2021-02-18 16:40:00 |
| Presentation Time |
25 minutes |
| Registration for |
ME |
| Paper # |
MMS2021-13, ME2021-13, AIT2021-13 |
| Volume (vol) |
vol.45 |
| Number (no) |
no.4 |
| Page |
pp.65-69 |
| #Pages |
5 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |