Feature Pairs Connected by Lines for Object Recognition
Muhammad Awais and Krystian Mikolajczyk
In: 20th International Conference on Patteren Recognition, 23-26 August 2010, Istanbul, Turkey.
In this paper we exploit image edges and segmentation
maps to build features for object category recognition.
We build a parametric line based image approximation
to identify the dominant edge structures. Line
ends are used as features described by histograms of
gradient orientations. We then form descriptors based
on connected line ends to incorporate weak topological
constraints which improve their discriminative power.
Using point pairs connected by an edge assures higher
repeatability than a random pair of points or edges.
The results are compared with state-of-the-art, and
show significant improvement on challenging recognition
benchmark Pascal VOC 2007. Kernel based fusion
is performed to emphasize the complementary nature of
our descriptors with respect to the state-of-the-art features.
|EPrint Type:||Conference or Workshop Item (Poster)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Muhammad Awais|
|Deposited On:||22 July 2011|