Name
Abstract | ||
|---|---|---|
Co-saliency detection is a task of detecting same or similar objects in multi-scene, and has been an important preprocessing step for multi-scene image processing. However existing methods lack efficiency to match similar areas from different images. In addition, they are confined to single image detection without a unified framework to calculate co-saliency. In this paper, we propose a novel model called Superpixel Matching-Cellular Automata (SMCA). We use Hausdorff distance adjacent superpixel sets instead of single superpixel since the feature matching accuracy of single superpixel is poor. We further introduce Cellular Automata to exploit the intrinsic relevance of similar regions through interactions with neighbors in multi-scene. Extensive evaluations show that the SMCA model achieves leading performance compared to state-of-the-art methods on both efficiency and accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3837/tiis.2017.05.015 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
co-saliency detection,multi-scene,superpixel,Hausdorff distance,Cellular Automata | Cellular automaton,Computer science,Salience (neuroscience),Theoretical computer science,Distributed computing | Journal |
Volume | Issue | ISSN |
11 | 5 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaofeng Zhang | 1 | 44 | 11.84 |
| Zemin Wu | 2 | 14 | 1.51 |
| Qingzhu Jiang | 3 | 0 | 0.34 |
| Lin Du | 4 | 8 | 2.14 |
| Lei Hu | 5 | 697 | 86.91 |