Beyond Local Appearance Category Recognition from Pairwise Interactions of Simple Features.pdf
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Beyond Local Appearance Category Recognition from Pairwise Interactions of Simple Features
Beyond Local Appearance: Category Recognition from Pairwise Interactions of
Simple Features
Marius Leordeanu1 Martial Hebert1 Rahul Sukthankar2,1
mleordea@ hebert@ rahuls@
1Carnegie Mellon University 2Intel Research Pittsburgh
Abstract
We present a discriminative shape-based algorithm for
object category localization and recognition. Our method
learns object models in a weakly-supervised fashion, with-
out requiring the specification of object locations nor pixel
masks in the training data. We represent object models
as cliques of fully-interconnected parts, exploiting only the
pairwise geometric relationships between them. The use
of pairwise relationships enables our algorithm to suc-
cessfully overcome several problems that are common to
previously-published methods. Even though our algorithm
can easily incorporate local appearance information from
richer features, we purposefully do not use them in or-
der to demonstrate that simple geometric relationships can
match (or exceed) the performance of state-of-the-art object
recognition algorithms.
1. Introduction
Object category recognition is very challenging because
there is no formal definition of what constitutes an object
category. While people largely agree on common, useful
categories, it is still not clear which are the objects’ features
that help us group them into such categories. Our proposed
approach is based on the observation that for a wide variety
of common object categories, shape matters more than lo-
cal appearance. For example, it is the shape, not the color
or texture, that enables a plane to fly, an animal to run or
a human hand to manipulate objects. Many categories are
defined by their function and it is typically the case that
function dictates an object’s shape rather than its low level
surface appearance. In this paper we represent these object
category models as cliques of very simple features (sparse
points and their normals), and focus only on the pairwise
geometric relationships betwee
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