Context models and out-of-context objects by Myung Jin Choia, Antonio Torralbab, Alan S. Willskyc.
Abstract:
The context of an image encapsulates rich information about how natural scenes and objects are related to each other. Such contextual information has the potential to enable a coherent understanding of natural scenes and images. However, context models have been evaluated mostly based on the improvement of object recognition performance even though it is only one of many ways to exploit contextual information. In this paper, we present a new scene understanding problem for evaluating and applying context models. We are interested in finding scenes and objects that are “out-of-context”. Detecting “out-of-context” objects and scenes is challenging because context violations can be detected only if the relationships between objects are carefully and precisely modeled. To address this problem, we evaluate different sources of context information, and present a graphical model that combines these sources. We show that physical support relationships between objects can provide useful contextual information for both object recognition and out-of-context detection.
The authors distinguish object recognition in surveillance video versus still photographs, the subject of the investigation here. A “snapshot” if you will.
Subjects in digital media, assuming you don’t have the authoring data stream, exist in “snapshots” of a sort don’t they?
To start with they are bound up in a digital artifact, which among other things lives in a file system, with a last modified date, amongst many other files.
There may be more “context” for subjects in digital files that appears at first blush. Will have to give that some thought.