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Measuring and Predicting Object Importance
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OA
AI
DOI：10.1007/s11263-010-0376-0.png)
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摘要
En 中文
How important is a particular object in a photograph of a complex scene? We propose a definition of importance and present two methods for measuring object importance from human observers. Using this ground truth, we fit a function for predicting the importance of each object directly from a segmented image; our function combines a large number of object-related and image-related features. We validate our importance predictions on 2,841 objects and find that the most important objects may be identified automatically. We find that object position and size are particularly informative, while a popular measure of saliency is not.
Keyword:
Visual recognition
Object recognition
Importance
Perception
Keywording
Saliency
Rank aggregation
Amazon Mechanical Turk