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

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Paper Detail

Paper IDIMT-1.4
Paper Title Automatic trimap generation by a multimodal neural network
Authors Masaki Taniguchi, Taro Tezuka, University of Tsukuba, Japan
SessionIMT-1: Computational Imaging Learning-based Models
LocationArea J
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In many of the existing alpha matting implementations, an intermediate representation called a trimap needs to be created manually. To automate the process, we propose a generic neural network for trimap generation based on saliency map detection. Our model multi-modally learns a saliency map and a trimap simultaneously. Because of this structure, the network focuses on reducing the error of the trimap especially within the areas with high salience. We used both the saliency map detection dataset and the alpha matting dataset to achieve accuracy in extracting subjects from natural images and generating trimaps. Experiments showed that our model could generate trimaps that are almost identical to manually generated ones. The method can also be easily combined with existing alpha matting algorithms.