.png){kind=icon}
Return
Visualization-Based Active Learning for Video Annotation
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2016.2614227.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Video annotation is an effective way to facilitate content-based analysis for videos. Automatic machine learning methods are commonly used to accomplish this task. Among these, active learning is one of the most effective methods, especially when the training data cost a great deal to obtain. One of the most challenging problems in active learning is the sample selection. Various sampling strategies can be used, such as uncertainty, density, and diversity, but it is difficult to strike a balance among them. In this paper, we provide a visualization-based batch mode sampling method to handle such a problem. An iso-contour-based scatterplot is used to provide intuitive clues for the representativeness and informativeness of samples and assist users in sample selection. A semisupervised metric learning method is incorporated to help generate an effective scatterplot reflecting the high-level semantic similarity for visual sample selection. Moreover, both quantitative and qualitative evaluations are provided to show that the visualization-based method can effectively enhance sample selection in active learning.
Keywords:
Active learning
projection
video annotation
visualization
Journal
IF:
9.7
Papers:
4.7K
Citations:
2.4W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveVisual deficits and cognitive assessment of multiple sclerosis: confounder, correlate, or both?
PREAI
ShareSave
View more