Large-Scale Object Classi cation using Label Relation Graphs by Jia Deng, et al.
Abstract:
In this paper we study how to perform object classi cation 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 classifi cation 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 classifi cation by exploiting the label relations.
Let’s hear it for “real world labels!”
By which the authors mean:
- An object can have more than one label.
- There are relationships between labels.
From the introduction:
We first introduce Hierarchy and Exclusion (HEX) graphs, a new formalism allowing flexible specifi cation of relations between labels applied to the same object: (1) mutual exclusion (e.g. an object cannot be dog and cat), (2) overlapping (e.g. a husky may or may not be a puppy and vice versa), and (3) subsumption (e.g. all huskies are dogs). We provide theoretical analysis on properties of HEX graphs such as consistency, equivalence, and computational implications.
Next, we propose a probabilistic classi fication model leveraging HEX graphs. In particular, it is a special type of Conditional Random Field (CRF) that encodes the label relations as pairwise potentials. We show that this model enjoys
a number of desirable properties, including flexible encoding of label relations, predictions consistent with label relations, efficient exact inference for typical graphs, learning labels with varying specifi city, knowledge transfer, and uni fication of existing models.
Having more than one label is trivially possible in topic maps. The more interesting case is the authors choosing to treat semantic labels as subjects and to define permitted associations between those subjects.
A world of possibilities opens up when you can treat something as a subject that can have relationships defined to other subjects. Noting that those relationships can also be treated as subjects should someone desire to do so.
I first saw this at: Is that husky a puppy?