2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-IVASR-4.6
Paper Title INTEREST LEVEL ESTIMATION VIA MULTI-MODAL GAUSSIAN PROCESS LATENT VARIABLE FACTORIZATION
Authors Kyohei Kamikawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionMLR-APPL-IVASR-4: Machine learning for image and video analysis, synthesis, and retrieval 4
LocationArea B
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a method of interest level estimation via multi-modal Gaussian process latent variable factorization (mGPLVF). The proposed method estimates user interest levels for contents with high accuracy by using multi-modal features such as contents and users' behavior. Generally, users' behavior includes some noise, and it is difficult to prepare a large amount of data. For dealing with the problem, the proposed method newly derives mGPLVF calculating appropriate latent variables that do not overfit a small amount of training data including noise based on a probabilistic generative model. Furthermore, mGPLVF simultaneously performs not only construction of the robust latent space but also estimation of user interest levels via the latent variables based on an idea inspired by a factorization machine. The consistent framework of latent space construction and interest level estimation leads to the improvement of the final estimation accuracy. Experimental results show the effectiveness of the proposed method.