Tukey-Inspired Video Object Segmentation
| Autor: | Jason J. Corso, Brent Griffin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Data source
Measure (data warehouse) Jaccard index business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Image segmentation Object (computer science) Visualization 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Segmentation Artificial intelligence business Reliability (statistics) |
| Zdroj: | WACV |
| DOI: | 10.1109/wacv.2019.00188 |
| Popis: | We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset. We find foreground objects in low-level vision data using a John Tukey-inspired measure of "outlierness." This Tukey-inspired measure also estimates the reliability of each data source as video characteristics change (e.g., a camera starts moving). The proposed method achieves state-of-the-art results for strictly unsupervised video object segmentation on the challenging DAVIS dataset. Finally, we use a variant of the Tukey-inspired measure to combine the output of multiple segmentation methods, including those using supervision during training, runtime, or both. This collectively more robust method of segmentation improves the Jaccard measure of its constituent methods by as much as 28%. |
| Databáze: | OpenAIRE |
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