Similarity-based cross-layered hierarchical representation for object categorization.
Marko Boben, Sanja Fidler and Aleš Leonardis
In: IEEE Computer Society Conference on Computer Vision and Pattern R 2008, 24-26 June 2008, Anchorage, Alaska.
This paper proposes a new concept in hierarchical representations
that exploits features of different granularity and
specificity coming from all layers of the hierarchy. The concept
is realized within a cross-layered compositional representation
learned from the visual data. We show how similarity
connections among discrete labels within and across
hierarchical layers can be established in order to produce a
set of layer-independent shape-terminals, i.e. shapinals. We
thus break the traditional notion of hierarchies and show
how the category-specific layers can make use of all the
necessary features stemming from all hierarchical layers.
This, on the one hand, brings higher generalization into the
representation, yet on the other hand, it also encodes the
notion of scales directly into the hierarchy, thus enabling a
multi-scale representation of object categories. By focusing
on shape information only, the approach is tested on
the Caltech 101 dataset demonstrating good performance
in comparison with other state-of-the-art methods.
|EPrint Type:||Conference or Workshop Item (Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Boris Horvat|
|Deposited On:||21 February 2012|