Archive for the ‘Context Models’ Category

Sarcastic Computers?

Thursday, May 31st, 2012

You may have seen the headline: Could Sarcastic Computers Be in Our Future? New Math Model Can Help Computers Understand Inference.

And the lead for the article sounds promising:

In a new paper, the researchers describe a mathematical model they created that helps predict pragmatic reasoning and may eventually lead to the manufacture of machines that can better understand inference, context and social rules.

Language is so much more than a string of words. To understand what someone means, you need context.

Consider the phrase, “Man on first.” It doesn’t make much sense unless you’re at a baseball game. Or imagine a sign outside a children’s boutique that reads, “Baby sale — One week only!” You easily infer from the situation that the store isn’t selling babies but advertising bargains on gear for them.

Present these widely quoted scenarios to a computer, however, and there would likely be a communication breakdown. Computers aren’t very good at pragmatics — how language is used in social situations.

But a pair of Stanford psychologists has taken the first steps toward changing that.

Context being one of those things you can use semantic mapping techniques to capture, I was interested.

Jack Park pointed me to a public PDF of the article: Predicting pragmatic reasoning in language games

Be sure to read the entire file.

A blue square, a blue circle, a green square.

Not exactly a general model for context and inference.

Context models and out-of-context objects

Saturday, May 5th, 2012

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.