Name
Abstract | ||
|---|---|---|
We consider video object cut as an ensemble of frame-level background-foreground object classifiers which fuses information across frames and refine their segmentation results in a collaborative and iterative manner. Our approach addresses the challenging issues of modeling of background with dynamic textures and segmentation of foreground objects from cluttered scenes. We construct patch-level bag-of-words background models to effectively capture the background motion and texture dynamics. We propose a foreground salience graph (FSG) to characterize the similarity of an image patch to the bag-of-words background models in the temporal domain and to neighboring image patches in the spatial domain. We incorporate this similarity information into a graph-cut energy minimization framework for foreground object segmentation. The background-foreground classification results at neighboring frames are fused together to construct a foreground probability map to update the graph weights. The resulting object shapes at neighboring frames are also used as constraints to guide the energy minimization process during graph cut. Our extensive experimental results and performance comparisons over a diverse set of challenging videos with dynamic scenes, including the new Change Detection Challenge Dataset, demonstrate that the proposed ensemble video object cut method outperforms various state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.254 | CVPR |
Keywords | Field | DocType |
bag-of-words background model,foreground probability map,neighboring frame,highly dynamic scenes,frame-level background-foreground object classifier,background motion,proposed ensemble video object,foreground object,resulting object,foreground object segmentation,ensemble video object cut,video object cut,image classification,graph theory,dynamics,probability,shape,graph cut,image texture,image segmentation | Cut,Background subtraction,Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Contextual image classification | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1063-6919 |
Citations | PageRank | References |
12 | 0.63 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaobo Ren | 1 | 102 | 11.14 |
| Tony X. Han | 2 | 1461 | 62.13 |
| Zhihai He | 3 | 1544 | 114.45 |