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.

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Deirdre O'Regan, Anil C. Kokaram
• Keywords: Algorithm Gentle Adaboost, Contextual Spatial Information, ICIP 2008, Image Processing, Markov Random Field, Object Detection, Two-class Bayesian Scheme