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2008
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Implicit spatial inference with sparse local features

9 years 4 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-class Bayesian scheme is used as a framework, and the likelihood is derived from the real-valued classification of machine learning algorithm Gentle AdaBoost, whose output is transformed to a probabilistic distribution using either of two models investigated; Log-Sigmoid or Bi-Gaussian. The main contribution is a novel scheme for the injection of prior contextual spatial information. This occurs on a uniquely designed Markov Random Field defined by Delaunay Triangulation of the feature points. Our experiments show that this framework is useful for object detection and segmentation, and we achieve good, mostly invariant results in these tasks.
Deirdre O'Regan, Anil C. Kokaram
Added 20 Oct 2009
Updated 20 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Deirdre O'Regan, Anil C. Kokaram
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