| Paper ID | MMSP-1.2 |
| Paper Title |
FEATURE INTEGRATION VIA SEMI-SUPERVISED ORDINALLY MULTI-MODAL GAUSSIAN PROCESS LATENT VARIABLE MODEL |
| Authors |
Kyohei Kamikawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan |
| Session | MMSP-1: Multimedia Signal Processing |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Multimedia Signal Processing: Multimedia Applications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This paper presents a method of feature integration via semi-supervised ordinally multi-modal Gaussian process latent variable model (Semi-OMGP). The proposed method transforms multi-modal features into common latent variables suitable for users’ interest level estimation. For dealing with the multi-modal features, the proposed method newly derives Semi-OMGP. Semi-OMGP has two contributions. First, Semi-OMGP is suitable for integration between heterogeneous modalities with different distributions by assuming that the similarity matrices of these modalities as observations are generated from latent variables. Second, Semi-OMGP can efficiently use label information by introducing an operator considering the ordinal grade into the prior distribution of latent variables when obtained label information is partially given. Semi-OMGP can simultaneously realize the above contributions, and successful multi-modal feature integration becomes feasible. Experimental results show the effectiveness of the proposed method. |
