ICML-98 Submission #125

Multiple-Instance Learning for Natural Scene Classification

Oded Maron
Aparna Lakshmi Ratan
Artificial Intelligence Lab 
M.I.T. 

Abstract

Multiple-Instance learning is a way of modeling ambiguity in
supervised learning examples.  Each example is a bag of instances, but
only the bag is labeled - not the individual instances.  A bag is
labeled negative if all the instances are negative, and positive if at
least one of the instances in positive.  We apply the
Multiple-Instance learning framework to the problem of learning how to
classify natural images.  Images are inherently ambiguous since they
can represent many different things.  A user labels an image as
positive if the image somehow contains the concept.  Each image is a
bag, and the instances are various sub-regions in the image.  From a
small collection of positive and negative examples, we can learn the
concept and then use it to retrieve images that contain the concept
from a large database.  We show that the Diverse Density algorithm
performs well in this task, that simple hypothesis classes are
sufficient to classify natural images, and that user interaction helps
to improve performance.

Keywords: Multiple-Instance learning, image classification, image
database retrieval 

Contact author: Oded Maron, oded@ai.mit.edu, (617) 253-1611