Name
Abstract | ||
|---|---|---|
We present a new segmentation method that leverages latent photographic information available at the moment of taking pictures. Photography on a portable device is often done by tapping to focus before shooting the picture. This tap-and-shoot interaction for photography not only specifies the region of interest but also yields useful focus/defocus cues for image segmentation. However, most of the previous interactive segmentation methods address the problem of image segmentation in a post-processing scenario without considering the action of taking pictures. We propose a learning-based approach to this new tap-and-shoot scenario of interactive segmentation. The experimental results on various datasets show that, by training a deep convolutional network to integrate the selection and focus/defocus cues, our method can achieve higher segmentation accuracy in comparison with existing interactive segmentation methods. |
Year | Venue | Field |
|---|---|---|
2018 | THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Shoot,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
21 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ding-Jie Chen | 1 | 31 | 6.70 |
| Jui-Ting Chien | 2 | 34 | 3.09 |
| Hwann-Tzong Chen | 3 | 826 | 52.13 |
| Long-Wen Chang | 4 | 532 | 51.82 |