Large-Scale Object Classification Using Label Relation Graphs
Abstract
. In this paper we study how to perform object classification in
a principled way that exploits the rich structure of real world labels. We
develop a new model that allows encoding of flexible relations between
labels. We introduce Hierarchy and Exclusion (HEX) graphs, a new formalism
that captures semantic relations between any two labels applied
to the same object: mutual exclusion, overlap and subsumption. We then
provide rigorous theoretical analysis that illustrates properties of HEX
graphs such as consistency, equivalence, and computational implications
of the graph structure. Next, we propose a probabilistic classification
model based on HEX graphs and show that it enjoys a number of desirable
properties. Finally, we evaluate our method using a large-scale
benchmark. Empirical results demonstrate that our model can signifi-
cantly improve object classification by exploiting the label relations.
a principled way that exploits the rich structure of real world labels. We
develop a new model that allows encoding of flexible relations between
labels. We introduce Hierarchy and Exclusion (HEX) graphs, a new formalism
that captures semantic relations between any two labels applied
to the same object: mutual exclusion, overlap and subsumption. We then
provide rigorous theoretical analysis that illustrates properties of HEX
graphs such as consistency, equivalence, and computational implications
of the graph structure. Next, we propose a probabilistic classification
model based on HEX graphs and show that it enjoys a number of desirable
properties. Finally, we evaluate our method using a large-scale
benchmark. Empirical results demonstrate that our model can signifi-
cantly improve object classification by exploiting the label relations.
Research Areas
