I come across these two papers from Zhang, et. al (2008) and Wu & Nevatia (2007). One of them classified the paper as local data association based, and the other is global data association based.
After reading the two papers, I assume that by local data association, they used local shape features to the result of foreground extraction such as edgelet features, and boosting edgelet based, and by global data association, they only used common (or should I say non-specific?) features, such as position, scale, appearance, and frame index. Could anyone make sure that my understanding is right? Also, is there any literature that I should read to enhance my comprehension on this context?
Thank you for your attention. I'm looking forward to your reply.
Think of it as local referring to your neighborhood. You know various things around there i.e. what you have learned over time. But if you move to a new neighbourhood you know a little about it. But you still know some things like the new neighbourhood would have a parking space for each house and things like that.
In the same way when you use specific features like some special shapes that are local data association because a shape of an apple would not fit a banana. But if you used some other feature you might be able to generalize your result over multiple inputs which is a case of global data association.