Locally Multiple-Instance Learning with Structured Bag Models
Jonathan Warrell and Philip Torr
In: EMMCVPR 2011, 25-27 July 2011, Saint Petersburg.
raditional approaches to Multiple-Instance Learning (MIL) operate under the assumption that the instances of a bag are generated independently, and therefore typically learn an instance-level classifier which does not take into account
possible dependencies between instances. This assumption is particularly inappropriate in visual data, where spatial dependencies are the norm. We introduce
here techniques for incorporating MIL constraints into Conditional Random Field models, thus providing a set of tools for constructing structured bag models, in which spatial (or other) dependencies are represented. Further, we show
how Deterministic Annealing, which has proved a successful method for training non-structured MIL models, can also form the basis of training models with structured bags. Results are given on various segmentation tasks.