MIS-Boost: Multiple Instance Selection Boosting

Details MIS-Boost: Multiple Instance Selection Boosting MIS-Boost: Multiple Instance Selection Boosting

2011-09-12

arxiv.org/abs/1109.2388v1

Computer Science

Learning

Scientific paper

In this paper, we present a new multiple instance learning (MIL) method, called MIS-Boost, which learns discriminative instance prototypes by explicit instance selection in a boosting framework. Unlike previous instance selection based MIL methods, we do not restrict the prototypes to a discrete set of training instances but allow them to take arbitrary values in the instance feature space. We also do not restrict the total number of prototypes and the number of selected-instances per bag; these quantities are completely data-driven. We show that MIS-Boost outperforms state-of-the-art MIL methods on a number of benchmark datasets. We also apply MIS-Boost to large-scale image classification, where we show that the automatically selected prototypes map to visually meaningful image regions.

Affiliated with

Also associated with

No associations

Rating

MIS-Boost: Multiple Instance Selection Boosting does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with MIS-Boost: Multiple Instance Selection Boosting, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and MIS-Boost: Multiple Instance Selection Boosting will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-59571