Name
Abstract | ||
|---|---|---|
This paper presents an effective algorithm, interactive 1-bit feedback segmentation using transductive inference (FSTI), that interactively reasons out image segmentation. In each round of interaction, FSTI queries the user one superpixel for acquiring 1-bit user feedback to define the label of that superpixel. The labeled superpixels collected so far are used to refine the segmentation and generate the next query. The key insight is treating the interactive segmentation as a transductive inference problem, and then suppressing the unnecessary queries via an intrinsic-graph-structure derived from transductive inference. The experiments conducted on five publicly available datasets show that selecting query superpixels concerning the intrinsic-graph-structure is helpful to improve the segmentation accuracy. In addition, an efficient boundary refinement is presented to improve segmentation quality by revising the misaligned boundaries of superpixels. The proposed FSTI algorithm provides a superior solution to the interactive image segmentation problem is evident. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s00138-018-0923-1 | Mach. Vis. Appl. |
Keywords | Field | DocType |
Interactive image segmentation,Transductive inference,Intrinsic-graph-structure | Transduction (machine learning),Pattern recognition,Computer science,Segmentation,Image segmentation,Artificial intelligence | Journal |
Volume | Issue | ISSN |
29 | 4 | 0932-8092 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ding-Jie Chen | 1 | 31 | 6.70 |
| Hwann-Tzong Chen | 2 | 826 | 52.13 |
| Long-Wen Chang | 3 | 532 | 51.82 |