Instance-Level Label Propagation with Multi-Instance Learning
Abstract
Label propagation is a popular semi-supervised
learning technique that transfers information from
labeled examples to unlabeled examples through a
graph. Most label propagation methods construct a
graph based on example-to-example similarity, assuming
that the resulting graph connects examples
that share similar labels. Unfortunately, examplelevel
similarity is sometimes badly defined. For
instance, two images may contain two different
objects, but have similar overall appearance due to
large similar background. In this case, computing
similarities based on whole-image would fail propagating
information to the right labels. This paper
proposes a novel Instance-Level Label Propagation
(ILLP) approach that integrates label propagation
with multi-instance learning. Each example is
treated as containing multiple instances, as in the
case of an image consisting of multiple regions.
We first construct a graph based on instancelevel
similarity and then simultaneously identify
the instances carrying the labels and propagate the
labels across instances in the graph. Optimization
is based on an iterative Expectation Maximization
(EM) algorithm. Experimental results on two
benchmark datasets demonstrate the effectiveness
of the proposed approach over several state-of-theart
methods.