Is that husky a puppy?
A computer’s ability to accurately classify objects in images is a fundamental research focus in computer vision. Current methods of classification typically adopt a multiclass method
, in which a detected object in an image is assigned a label from amongst a set of mutually exclusive labels. However, this does not capture the complexity of objects in the real world; For example, “husky”, “dog”, and “puppy” are not mutually exclusive, but while “husky” is automatically a “dog”, it may or may not be a “puppy”.
In Large-Scale Object Classification using Label Relation Graphs
, University of Michigan Assistant Professor +Jia Deng
along with Google co-authors Nan Ding, +Yangqing Jia
, Andrea Frome, +Kevin Murphy
, +Samy Bengio
, Yuan Li, +Hartmut Neven
, and +Hartwig Adam
introduce Hierarchy and Exclusion (HEX) graphs, a new formalism allowing flexible specification of relations between labels applied to objects in images.
Using prior knowledge about semantic label relations, the HEX graph formalism allows a “flexible specification 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)”
, providing a unified classification framework that generalizes existing models.
This work will be presented at the 2014 European Conference in Computer Vision (ECCV ‘14, http://goo.gl/DSOfeC
), hosted in Zurich, Switzerland, September 6-12. To learn more, read the full paper at http://goo.gl/1O6RqT