Generating reliable video annotations by exploiting the crowd
| Autor: | Roberto Di Salvo, Daniela Giordano, Concetto Spampinato |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science Interactive video 020207 software engineering 02 engineering and technology Object (computer science) Machine learning computer.software_genre Crowdsourcing Visualization Task (project management) Video tracking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Noisy data computer |
| Zdroj: | WACV |
| DOI: | 10.1109/wacv.2016.7477718 |
| Popis: | In computer vision and machine learning, the availability of annotated datasets is of crucial importance for both learning and performance evaluation. However, annotating visual datasets is a tedious and error-prone task and computer vision researchers usually dedicate a large amount of their time for collecting and generating annotations, which most of the time cannot be re-used in other scenarios. In this paper, we propose a simple, but effective, interactive video object segmentation method exploiting large noisy data gathered from crowd of users while playing a web game. Experimental results, carried out on two challenging video benchmarks, show how it is possible to generate reliable object segmentations in videos with a small human effort, achieving an accuracy comparable to the one obtained with manually-labeled annotations and also outperforming state-of-the-art video object segmentation approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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