Image Categorization by Learning and Reasoning with Regions
Yixin Chen, James Z. Wang; 5(Aug):913--939, 2004.
Abstract
Designing computer programs to automatically categorize images
using low-level features is a challenging research topic in
computer vision. In this paper, we present a new learning technique,
which extends Multiple-Instance Learning (MIL), and its application
to the problem of region-based image categorization. Images are
viewed as bags, each of which contains a number of instances
corresponding to regions obtained from image segmentation. The
standard MIL problem assumes that a bag is labeled positive if at
least one of its instances is positive; otherwise, the bag is
negative. In the proposed MIL framework, DD-SVM, a bag label
is determined by some number of instances satisfying various
properties. DD-SVM first learns a collection of
instance
prototypes according to a Diverse Density (DD) function. Each
instance prototype represents a class of instances that is more
likely to appear in bags with the specific label than in the
other bags. A nonlinear mapping is then defined using the instance
prototypes and maps every bag to a point in a new feature space,
named the
bag feature space. Finally, standard support
vector machines are trained in the bag feature space. We provide
experimental results on an image categorization problem and a
drug activity prediction problem.
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