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 Tri- angulation 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.